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What Is Tourism?

Before engaging in a study of tourism, let&#;s have a closer look at what this term means.

Definition of Tourism

There are a number of ways tourism can be defined, and for this reason, the United Nations World Tourism Organization(UNWTO) embarked on a project from to to create a common glossary of terms for tourism. It defines tourism as follows:

Tourism is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes. These people are called visitors (which may be either tourists or excursionists; residents or non-residents) and tourism has to do with their activities, some of which imply tourism expenditure (United Nations World Tourism Organization, ).

Using this definition, we can see that tourism is the movement of people for a number of purposes (whether business or pleasure).

Definition of Tourist

Building on the definition of tourism, a commonly accepted description of a tourist is &#;someone who travels at least 80 km from his or her home for at least 24 hours, for business or leisure or other reasons&#; (LinkBC, , p.8). The United Nations World Tourism Organization () helps us break down this definition further by stating tourists can be:

1. Domestic (residents of a given country travelling only within that country)
2. Inbound (non-residents travelling in a given country)
3. Outbound (residents of one country travelling in another country)

The scope of tourism, therefore, is broad and encompasses a number of activities.

NAICS: The North American Industry Classification System

Given the sheer size of the tourism industry, it can be helpful to break it down into broad industry groups using a common classification system. The North American Industry Classification System (NAICS) was jointly created by the Canadian, US, and Mexican governments to ensure common analysis across all three countries (British Columbia Ministry of Jobs, Tourism and Skills Training, a). The tourism-related groupings created using NAICS are (in alphabetical order):

1. Accommodation
2. Food and beverage services (commonly known as &#;F & B&#;)
3. Recreation and entertainment
4. Transportation
5. Travel services

These industry groups are based on the similarity of the &#;labour processes and inputs&#; used for each (Government of Canada, ). For instance, the types of employees and resources required to run an accommodation business &#; whether it be a hotel, motel, or even a campground &#; are quite similar. All these businesses need staff to check in guests, provide housekeeping, employ maintenance workers, and provide a place for people to sleep. As such, they can be grouped together under the heading of accommodation. The same is true of the other four groupings, and the rest of this text explores these industry groups, and other aspects of tourism, in more detail. 

The Hospitality Industry

When looking at tourism it&#;s important to consider the term hospitality. Some define hospitality as &#;the business of helping people to feel welcome and relaxed and to enjoy themselves&#; (Discover Hospitality, , ¶ 3). Simply put, the hospitality industry is the combination of the accommodation and food and beverage groupings, collectively making up the largest segment of the industry. You&#;ll learn more about accommodations and F & B in Chapter 3 and Chapter 4, respectively. 

Before we seek to understand the five industry groupings in more detail, it&#;s important to have an overview of the history and impacts of tourism to date.

Global Overview

Origins of Tourism

Travel for leisure purposes has evolved from an experience reserved for very few people into something enjoyed by many. Historically, the ability to travel was reserved for royalty and the upper classes. From ancient Roman times through to the 17th century, young men of high standing were encouraged to travel through Europe on a &#;grand tour&#; (Chaney, ). Through the Middle Ages, many societies encouraged the practice of religious pilgrimage, as reflected in Chaucer&#;s Canterbury Tales and other literature.

The word hospitality predates the use of the word tourism, and first appeared in the 14th century. It is derived from the Latin hospes, which encompasses the words guest, host, and foreigner (Latdict, ). The word tourist appeared in print much later, in  (Griffiths and Griffiths, ). William Theobald suggests that the word tour comes from Greek and Latin words for circle and turn, and that tourism and tourist represent the activities ofcircling away from home, and then returning (Theobald, ).

Tourism Becomes Business

Cox & Kings, the first known travel agency, was founded in when Richard Cox became official travel agent of the British Royal Armed Forces (Cox & Kings, ).  Almost  years later, in June , Thomas Cook opened the first leisure travel agency, designed to help Britons improve their lives by seeing the world and participating in the temperance movement. In , he ran his first commercial packaged tour, complete with cost-effective railway tickets and a printed guide (Thomas Cook, ).

The continued popularity of rail travel and the emergence of the automobile presented additional milestones in the development of tourism. In fact, a long journey taken by Karl Benz&#;s wife in served to kick off interest in auto travel and helped to publicize his budding car company, which would one day become Mercedes Benz (Auer, ). We take a closer look at the importance of car travel later this chapter, and of transportation to the tourism industry in Chapter 2.

Fast forward to with the first commercial air flights from London, England, to Johannesburg, South Africa, and Colombo, Sri Lanka (Flightglobal, ) and the dawn of the jet age, which many herald as the start of the modern tourism industry. The s also saw the creation of Club Méditérannée (Gyr, ) and similar club holiday destinations, the precursor of today&#;s all-inclusive resorts.

The decade that followed is considered to have been a significant period in tourism development, as more travel companies came onto the scene, increasing competition for customers and moving toward &#;mass tourism, introducing new destinations and modes of holidaying&#; (Gyr, , p. 32).

Industry growth has been interrupted at several key points in history, including World War I, the Great Depression, and World War II. At the start of this century, global events thrust international travel into decline including the September 11, , attack on the World Trade Center in New York City (known as 9/11), the war in Iraq, perceived threat of future terrorist attacks, and health scares including SARS, BSE (bovine spongiform encephalopathy), and West Nile virus (Government of Canada, ).

At the same time, the industry began a massive technological shift as increased internet use revolutionized travel services. Through the s, online travel bookings grew exponentially, and by  global leader Expedia had expanded to include brands such as standardservices.com.pk, the Hotwire Group, trivago, and Expedia CruiseShip Centers, earning revenues of over $ million (Expedia Inc., ).

A more in-depth exploration of the impact of the online marketplace, and other trends in global tourism, is provided in Chapter But as you can already see, the impacts of the global tourism industry today are impressive and far reaching. Let&#;s have a closer look at some of these outcomes.

Tourism Impacts

Tourism impacts can be grouped into three main categories: economic, social, and environmental. These impacts are analyzed using data gathered by businesses, governments, and industry organizations.

Economic Impacts

According to a UNWTO report, in , &#;international tourism receipts exceeded US$1 trillion for the first time&#; (UNWTO, ). UNWTO Secretary-General Taleb Rifai stated this excess of $1 trillion was especially important news given the global economic crisis of , as tourism could help rebuild still-struggling economies, because it is a key export and labour intensive (UNWTO, ). 

Tourism around the world is now worth over $1 trillion annually, and it&#;s a growing industry almost everywhere. Regions with the highest growth in terms of tourism dollars earned are the Americas, Europe, Asia and the Pacific, and Africa. Only the Middle East posted negative growth at the time of the report (UNWTO, ).

While North and South America are growing the fastest, Europe continues to lead the way in terms of overall percentage of dollars earned (UNWTO, ):

• Europe (45%)
• Asia and the Pacific (28%)
• North and South America (19%)
• Middle East (4%)

Global industry growth and high receipts are expected to continue. In its August expenditure barometer, the UNWTO found worldwide visitation had increased by 22 million people in the first half of the year over the previous year, to reach million visits (UNWTO, a). As well, the UNWTO&#;s Tourism Vision predicts that international arrivals will reach nearly billion by  Read more about the Tourism Vision: standardservices.com.pk

Social Impacts

In addition to the economic benefits of tourism development, positive social impacts include an increase in amenities (e.g., parks, recreation facilities), investment in arts and culture, celebration of First Nations people, and community pride. When developed conscientiously, tourism can, and does, contribute to a positive quality of life for residents.

However, as identified by the United Nations Environment Programme (UNEP, a), negative social impacts of tourism can include:

• Change or loss of indigenous identity and values
• Culture clashes
• Physical causes of social stress (increased demand for resources)
• Ethical issues (such as an increase in sex tourism or the exploitation of child workers)

Some of these issues are explored in further detail in Chapter 12, which examines the development of Aboriginal tourism in British Columbia.

Environmental Impacts

Tourism relies on, and greatly impacts, the natural environment in which it operates. Even though many areas of the world are conserved in the form of parks and protected areas, tourism development can have severe negative impacts. According to UNEP (b), these can include:

• Depletion of natural resources (water, forests, etc.)
• Pollution (air pollution, noise, sewage, waste and littering)
• Physical impacts (construction activities, marina development, trampling, loss of biodiversity)

The environmental impacts of tourism can reach outside local areas and have an effect on the global ecosystem. One example is increased air travel, which is a major contributor to climate change. Chapter 10 looks at the environmental impacts of tourism in more detail.

Whether positive or negative, tourism is a force for change around the world, and the industry is transforming at a staggering rate. But before we delve deeper into our understanding of tourism, let&#;s take a look at the development of the sector in our own backyard.

Canada Overview

Origins of Tourism in Canada

Tourism has long been a source of economic development for our country. Some argue that as early as the explorers of the day, such as Jacques Cartier, were Canada&#;s first tourists (Dawson, ), but most agree the major developments in Canada&#;s tourism industry followed milestones in the transportation sector: by rail, by car, and eventually, in the skies.

Railway Travel: The Ties That Bind

The dawn of the railway age in Canada came midway through the 19th century. The first railway was launched in (Library and Archives Canada, n.d.), and by the onset of World War I in , four railways dominated the Canadian landscape: Canadian Pacific Railway (CPR), Canadian Northern Railway (CNOR), the Grand Trunk Railway (GTR), and the Grand Trunk Pacific (GTP). Unfortunately, their rapid expansion soon brought the last three into near bankruptcy (Library and Archives Canada, n.d.).

In , these three rail companies were amalgamated into the Canadian National Railway (CNR), and together with the CPR, these trans-continentals dominated the Canadian travel landscape until other forms of transportation became more popular. In , with declining interest in rail travel, the CPR and CNR were forced to combine their passenger services to form VIA Rail (Library and Archives Canada, n.d.).

The Rise of the Automobile

The rising popularity of car travel was partially to blame for the decline in rail travel, although it took time to develop. When the first cross-country road trip took place in , there were only 16 kilometres of paved road across Canada (MacEachern, ). Cars were initially considered a nuisance, and the National Parks Branch banned entry to automobiles, but later slowly began to embrace them. By the s, some parks, such as Cape Breton Highlands National Park, were actually created to provide visitors with scenic drives (MacEachern, ).

It would take decades before a coast-to-coast highway was created, with the Trans-Canada Highway officially opening in Revelstoke in When it was fully completed in , it was the longest national highway in the world, spanning one-fifth of the globe (MacEachern, ).

Early Tourism Promotion

As early as , enterprising Canadians like the Brewsters became the country&#;s first tour operators, leading guests through areas such as Banff National Park (Brewster Travel Canada, ). Communities across Canada developed their own marketing strategies as transportation development took hold. For instance, the town of Maisonneuve in Quebec launched a campaign from to calling itself &#;Le Pittsburg du Canada.&#; And by  Quebec was spending $, promoting tourism, with Ontario, New Brunswick, and Nova Scotia also enjoying established provincial tourism bureaus (Dawson, ).

National Airlines

Our national airline, Air Canada, was formed in as Trans-Canada Air Lines. In many ways, Air Canada was a world leader in passenger aviation, introducing the world’s first computerized reservations system in (Globe and Mail, ). Through the s and s, reduced airfares saw increased mass travel. Competitors including Canadian Pacific (which became Canadian Airlines in ) began to launch international flights during this time to Australia, Japan, and South America (Canadian Geographic, ). By , Air Canada was facing financial peril and forced to restructure. A numbered company, owned in part by Air Canada, purchased 82% of Canadian Airline’s shares, with the result of Air Canada becoming the country’s only national airline (Canadian Geographic, ).

Parks and Protected Areas

A look at the evolution of tourism in Canada would be incomplete without a quick study of our national parks and protected areas. The official conserving of our natural spaces began around the same time as the railway boom, and in Banff was established as Canada&#;s first national park. By , the Dominion Forest Reserves and Parks Act created the Dominion Parks Branch, the first of its kind in the world (Shoalts, ).

The systemic conservation and celebration of Canada&#;s parks over the next century would help shape Canada&#;s identity, both at home and abroad. Through the s, conservation officers and interpreters were hired to enhance visitor experiences. By , the National Park System Plan divided Canada into 39 regions, with the goal of preserving each distinct ecosystem for future generations. In , the country&#;s first national marine park was established in Ontario, and in the 20 years that followed, 10 new national parks and marine conservation areas were created (Shoalts, ).

The role of parks and protected areas in tourism is explored in greater detail in Chapter 5 (recreation) and Chapter 10 (environmental stewardship).

Global Shock and Industry Decline

As with the global industry, Canada&#;s tourism industry was impacted by world events such as the Great Depression and the World Wars.

More recently, global events such as 9/11, the SARS outbreak, and the war in Iraq took their toll on tourism receipts. Worldwide arrivals to Canada dropped 1% to million in , after three years of stagnant growth. In , spending reached $ billion with domestic travel accounting for 71% (Government of Canada, ).

Tourism in Canada Today

In , tourism created $ billion in total economic activity and , jobs. Tourism accounted for more of Canada&#;s gross domestic product (GDP) than agriculture, forestry, and fisheries combined (Tourism Industry Association of Canada, ).

Unfortunately, while overall receipts from tourism appear healthy, and globally the industry is growing, according to a recent report, Canada&#;s historic reliance on the US market (which traditionally accounts for 75% of our market) is troubling. Because three out of every four international visitors to Canada originates in the United States, the 55% decline in that market since is being very strongly felt here. Many feel the decline in American visitors to Canada can be attributed to tighter passport and border regulations, the economic downturn (including the global economic crisis), and a stronger Canadian dollar (TIAC, ).

Despite disappointing numbers from the United States, Canada continues to see strong visitation from the United Kingdom, France, Germany, Australia, and China. In , we welcomed 3,, tourists from our top 15 inbound countries (excluding the United States). Canadians travelling domestically accounted for 80% of tourism revenues in the country, and TIAC suggested that a focus on rebounding US visitation would help grow the industry (TIAC, ).

As organizations like TIAC work to confront barriers to travel, the Canadian Tourism Commission (CTC) is active abroad, encouraging more visitors to explore our country. In Chapter 8, we&#;ll delve more into the challenges and triumphs of selling tourism at home and abroad.

The great news for British Columbia is that once in Canada, most international visitors tend to remain in the province they landed in, and BC is one of three provinces that receives the bulk of this traffic (TIAC, ). In fact, BC&#;s tourism industry is one of the healthiest in Canada today. Let&#;s have a look at how our provincial industry was established and where it stands now.

British Columbia Overview

Origins of Tourism in BC

As with the history of tourism in Canada, it&#;s often stated that the first tourists to BC were explorers. In , Captain James Cook touched down on Vancouver Island, followed by James Douglas in , a British agent who had been sent to find new headquarters for the Hudson&#;s Bay Company, ultimately choosing Victoria. Through the s, BC&#;s gold rush attracted prospectors from around the world, with towns and economies springing up along the trail (PricewaterhouseCoopers, ).

Railway Travel: Full Steam Ahead!

The development of BC&#;s tourism industry began in earnest in the late s when the CPR built accommodation properties along itsnewly completed trans-Canada route, capturing revenues from overnight stays to help alleviate their increasing corporate debt. Following the construction of small lodges at stops in Field, Rogers Pass, and Fraser Canyon, the CPR opened the Hotel Vancouver in May (Dawson, ).

As opposed to Atlantic Canada, where tourism promotion centred around attracting hunters and fishermen for a temporary infusion of cash, in British Columbia tourism was seen as a way to lure farmers and settlers to stay in the new province. Industry associations began to form quickly: the Tourist Association of Victoria (TAV) in February , and the Vancouver Tourist Association in June of the same year (Dawson, ).

Many of the campaigns struck by these and other organizations between and centred on the province&#;s natural assets, as people sought to escape modern convenience and enjoy the environment. A collaborative group called the Pacific Northwest Travel Association (BC, Washington, and Oregon) promoted &#;The Pacific Northwest: The World&#;s Greatest Out of Doors,&#; calling BC &#;The Switzerland of North America.&#; Promotions like these seemed to have had an effect: in , over , tourists visited Victoria, spending over $ million (Dawson, ).

The Great Depression and World War II

As the world&#;s economy was sent into peril during the Great Depression in the s, tourism was seen as an economic solution. A newly renamed Greater Victoria Publicity Bureau touted a &#; for 1&#; multiplier effect of tourism spending, with visitor revenues accounting for around % of BC&#;s income in  By , an organization known as the TTDA (Tourist Trade Development Association of Victoria and Vancouver Island) looked to create a more stable industry through strategies to increase visitors&#; length of stay (Dawson, ).

In , the provincial Bureau of Industrial and Tourist Development (BITD) was formed through special legislation with a goal of increasing tourist traffic. By , the organization changed its name to the British Columbia Government Travel Bureau (BCGTB) and was granted a budget increase to $, This was soon followed by an expansion of the BC Tourist Council designed to solicit input from across the province. And in , Vancouver welcomed the King and Queen of England and celebrated the opening of the Lions Gate Bridge, activities that reportedly bolstered tourism numbers (Dawson, ).

The December Japanese attack on Pearl Harbor in Hawaii had negative repercussions for tourism on the Pacific Rim and was responsible for an era of decreased visitation to British Columbia, despite attempts by some to market the region as exciting. From to , US visits to Vancouver (measured at the border) dropped from over , to approximately , Just two years later, however, that number jumped to ,, the result of campaigns like the initiative aimed at Americans that marketed BC as &#;comrades in war&#; (Dawson, ).

Post-War Rebound

We, with all due modesty, cannot help but claim that we are entering British Columbia&#;s half-century, and cannot help but observe that B.C. also stands for BOOM COUNTRY. &#; Phil Gagliardi, BC Minister of Highways, (Dawson, , p)

A burst of post-war spending began in , and although short-lived, was supported by steady government investment in marketing throughout the s. As tourism grew in BC, however, so did competition for US dollars from Mexico, the Caribbean, and Europe. The decade that followed saw an emphasis on promoting BC&#;s history, its &#;Britishness,&#; and a commodification of Aboriginal culture. The BCGTB began marketing efforts to extend the travel season, encouraging travel in September, prime fishing season. It also tried to push visitors to specific areas, including the Lower Fraser Valley, the Okanagan-Fraser Canyon Loop, and the Kamloops-Cariboo region (Dawson, ).

In , Vancouver hosted the British Empire Games, investing in the construction of Empire Stadium. A few years later, an increased emphasis on events and convention business saw the Greater Vancouver Tourist Association change its name in to the Greater Vancouver Visitors and Convention Bureau (PricewaterhouseCoopers, ).

The ski industry was also on the rise: in , the lodge and chairlift on Tod Mountain (now Sun Peaks) opened, and Whistler followed suit five years later (PricewaterhouseCoopers, ). Ski partners became pioneers of collaborative marketing in the province with the foundation of the Ski Marketing Advisory Committee (SMAC) supported by Tod Mountain and Big White, evolving into today&#;s Canada’s West Ski Area Association (Magnes, ). This pioneer spirit was evident across the ski sector: the entire sport of heliskiing was invented by Hans Gosmer of BC&#;s Canadian Mountain Holidays, and today the province holds 90% of the world&#;s heliskiing market share (McLeish, ).

The concept of collaboration extended throughout the province as innovative funding structures saw the cost of marketing programs shared between government and industry in BC. These programs were distributed through regional channels (originally eight regions in the province), and considered “the most constructive and forward looking plan of its kind in Canada” (Dawson , p).

Tourism in BC continued to grow through the s. In , the Hotel Room Tax Act was introduced, allowing for a 5% tax to be collected on room nights with the funds collected to be put toward marketing and development. By , construction had begun on Whistler Village, with Blackcomb Mountain opening two years later (PricewaterhouseCoopers, ). Funding programs in the late s and early s such as the Canada BC Tourism Agreement (CBCTA) and Travel Industry Development Subsidiary Agreement (TIDSA) allowed communities to invest in projects that would make them more attractive tourism destinations. In the mountain community of Kimberley, for instance, the following improvements were implemented through a $ million forgivable loan: a new road to the ski resort, a covered tennis court, a mountain lodge, an alpine slide, and nine more holes for the golf course (e-Know, ).

Around the same time, the &#;Super, Natural British Columbia&#; brand was introduced, and a formal bid was approved for Vancouver to host a fair then known as Transpo 86 (later Expo 86). Tourism in the province was about to truly take off.

Expo 86 and Beyond

By the time the world fair Expo 86 came to a close in October , it had played host to 20,, guests. Infrastructure developments, including rapid rail, airport improvements, a new trade and convention centre at Canada Place (with a cruise ship terminal), and hotel construction, had positioned the city and the province for further growth (PricewaterhouseCooopers, ). The construction and opening of the Coquihalla Highway through to enhanced the travel experience and reduced travel times to vast sections of the province (Magnes, ).

By , Vancouver International Airport (YVR) was named number one in the world by the International Air Transport Association’s survey of international passengers. Five years later, the airport welcomed a record million passengers (PricewaterhouseCoopers, ).

Going for Gold

In , the International Olympic Committee named Vancouver/Whistler as the host city for the Olympic and Paralympic Winter Games. Infrastructure development followed, including the expansion of the Sea-to-Sky Highway, the creation of Vancouver Convention Centre West, and the construction of the Canada Line, a rapid transport line connecting the airport with the city&#;s downtown.

As BC prepared to host the Games, its international reputation continued to grow. Vancouver was voted “Best City in the Americas” by Condé Nast Traveller magazine three years in a row. Kelowna was named “Best Canadian Golf City” by Canada’s largest golf magazine, and BC was named the “Best Golf Destination in North America” by the International Association of Golf Tour Operators. Kamloops, known as Canada’s Tournament City, hosted over sports tournaments that same year, and nearby Sun Peaks Resort was named the “Best Family Resort in North America” by the Great Skiing and Snowboarding Guide in (PricewaterhouseCoopers, ).

By the time the Vancouver Olympic and Paralympic Games took place, over 80 participating countries, 6, athletes, and 3 billion viewers put British Columbia on centre stage.

Tourism in BC Today

Building on the momentum generated by hosting the Winter Olympic Games, tourism in BC remains big business. In , the industry generated $ billion in revenue.

The provincial industry is made up of over 18, businesses, the majority of which are SMEs (small to medium enterprises), and together they employ approximately , people (Tourism Industry Association of BC, ). It may surprise you to learn that in British Columbia, tourism provides more jobs than high tech, oil and gas, mining, and forestry (Porges, ).

One of the challenges for BC&#;s tourism industry, it has long been argued, is fragmentation. Back in September , an article in the Victoria Daily Times argued for more coordination across organizations in order to capitalize on what they saw as Canada&#;s &#;largest dividend payer&#; (Dawson, ). Today, more than 80 years later, you will often hear BC tourism professionals say the same thing.

On the other hand, some experts believe that the industry is simply a model of diversity, acknowledging that tourism is a compilation of a multitude of businesses, services, organizations, and communities. They see the ways in which these components are working together toward success, rather than focusing on friction between the groups.

Many communities are placing a renewed focus on educating the general public and other businesses about the value of tourism and the ways in which stakeholders work together. The following case study highlights this in more detail:

Throughout the rest of this textbook, you&#;ll have a chance to learn more about the history and current outlook for tourism in BC, with in-depth coverage of some of the triumphs and challenges we&#;ve faced as an industry. You will also learn about the Canadian and global contexts of the tourism industry&#;s development.

Conclusion

As we&#;ve seen in this chapter, tourism is a complex set of industries including accommodation, recreation and entertainment, food and beverage services, transportation, and travel services. It encompasses domestic, inbound, and outbound travel for business, leisure, or other purposes. And because of this large scope, tourism development requires participation from all walks of life, including private business, governmental agencies, educational institutions, communities, and citizens.

Recognizing the diverse nature of the industry and the significant contributions tourism makes toward economic and social value for British Columbians is important. There remains a great deal of work to better educate members of the tourism industry, other sectors, and the public about the ways tourism contributes to our province.

Given this opportunity for greater awareness, it is hoped that students like you will help share this information as you learn more about the sector. So let&#;s begin our exploration in Chapter 2 with a closer look at a critical sector: transportation.

References

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Attributions

Figure  Selkirk College and Nelson by LinkBC is used under a CC-BY  license.

Figure Capilano University&#;s Team by LinkBC is used under a CC-BY  license.

Figure Vancouver Island University by LinkBC is used under a CC-BY  license.

Figure  Canadian Pacific Am No  by Peter Broster is used under a CC-BY  license.

Figure Vancouver Island University by LinkBC is used under a CC-BY  license.

Figure Switzerland vs. Canada by standardservices.com.pk is used under a CC-BY  license.

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A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item.^[1][2] They are primarily used in commercial applications.

Recommender systems are utilized in a variety of areas and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter.^[3] These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts,^[4] collaborators,^[5] and financial services.^[6]

Overview[edit]

Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also known as the personality-based approach),^[7] as well as other systems such as knowledge-based systems. Collaborative filtering approaches build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in.^[8] Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties.^[9] Current recommender systems typically combine one or more approaches into a hybrid system.

The differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems – standardservices.com.pk and Pandora Radio.

• standardservices.com.pk creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. standardservices.com.pk will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique.
• Pandora uses the properties of a song or artist (a subset of the attributes provided by the Music Genome Project) to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach.

Each type of system has its strengths and weaknesses. In the above example, standardservices.com.pk requires a large amount of information about a user to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems.^{[10][11][12][13]} Whereas Pandora needs very little information to start, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed).

Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data.

Recommender systems were first mentioned in a technical report as a "digital bookshelf" in by Jussi Karlgren at Columbia University,^[14] and implemented at scale and worked through in technical reports and publications from onwards by Jussi Karlgren, then at SICS,^[15][16] and research groups led by Pattie Maes at MIT,^[17] Will Hill at Bellcore,^[18] and Paul Resnick, also at MIT^[19][20] whose work with GroupLens was awarded the ACM Software Systems Award.

Montaner provided the first overview of recommender systems from an intelligent agent perspective.^[21] Adomavicius provided a new, alternate overview of recommender systems.^[22] Herlocker provides an additional overview of evaluation techniques for recommender systems,^[23] and Beel et al. discussed the problems of offline evaluations.^[24] Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges.^[25][26][27]

Recommender systems have been the focus of several granted patents. ^{[28][29][30][31][32]}

Approaches[edit]

Collaborative filtering[edit]

An example of collaborative filtering based on a ratings system

One approach to the design of recommender systems that has wide use is collaborative filtering.^[33] Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. The system generates recommendations using only information about rating profiles for different users or items. By locating peer users/items with a rating history similar to the current user or item, they generate recommendations using this neighborhood. Collaborative filtering methods are classified as memory-based and model-based. A well-known example of memory-based approaches is the user-based algorithm,^[34] while that of model-based approaches is the Kernel-Mapping Recommender.^[35]

A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach^[36] and the Pearson Correlation as first implemented by Allen.^[37]

When building a model from a user's behavior, a distinction is often made between explicit and implicit forms of data collection.

Examples of explicit data collection include the following:

• Asking a user to rate an item on a sliding scale.
• Asking a user to search.
• Asking a user to rank a collection of items from favorite to least favorite.
• Presenting two items to a user and asking him/her to choose the better one of them.
• Asking a user to create a list of items that he/she likes (see Rocchio classification or other similar techniques).

Examples of implicit data collection include the following:

• Observing the items that a user views in an online store.
• Analyzing item/user viewing times.^[38]
• Keeping a record of the items that a user purchases online.
• Obtaining a list of items that a user has listened to or watched on his/her computer.
• Analyzing the user's social network and discovering similar likes and dislikes.

Collaborative filtering approaches often suffer from three problems: cold start, scalability, and sparsity.^[39]

• Cold start: For a new user or item, there isn't enough data to make accurate recommendations.^[10][11][12]
• Scalability: In many of the environments in which these systems make recommendations, there are millions of users and products. Thus, a large amount of computation power is often necessary to calculate recommendations.
• Sparsity: The number of items sold on major e-commerce sites is extremely large. The most active users will only have rated a small subset of the overall database. Thus, even the most popular items have very few ratings.

One of the most famous examples of collaborative filtering is item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by standardservices.com.pk's recommender system.^[40]

Many social networks originally used collaborative filtering to recommend new friends, groups, and other social connections by examining the network of connections between a user and their friends.^[1] Collaborative filtering is still used as part of hybrid systems.

Content-based filtering[edit]

Another common approach when designing recommender systems is content-based filtering. Content-based filtering methods are based on a description of the item and a profile of the user's preferences.^[41][42] These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.

In this system, keywords are used to describe the items and a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past, or is examining in the present. It does not rely on a user sign-in mechanism to generate this often temporary profile. In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended. This approach has its roots in information retrieval and information filtering research.

To create a user profile, the system mostly focuses on two types of information:

1. A model of the user's preference.

2. A history of the user's interaction with the recommender system.

Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation).^[43] The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks in order to estimate the probability that the user is going to like the item.^[44]

A key issue with content-based filtering is whether the system is able to learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended. For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc. from different services can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of hybrid system.

Content-based recommender systems can also include opinion-based recommender systems. In some cases, users are allowed to leave text review or feedback on the items. These user-generated texts are implicit data for the recommender system because they are potentially rich resource of both feature/aspects of the item, and users' evaluation/sentiment to the item. Features extracted from the user-generated reviews are improved meta-data of items, because as they also reflect aspects of the item like meta-data, extracted features are widely concerned by the users. Sentiments extracted from the reviews can be seen as users' rating scores on the corresponding features. Popular approaches of opinion-based recommender system utilize various techniques including text mining, information retrieval, sentiment analysis (see also Multimodal sentiment analysis) and deep learning ^[45].

Multi-criteria recommender systems[edit]

Multi-criteria recommender systems (MCRS) can be defined as recommender systems that incorporate preference information upon multiple criteria. Instead of developing recommendation techniques based on a single criterion value, the overall preference of user u for the item i, these systems try to predict a rating for unexplored items of u by exploiting preference information on multiple criteria that affect this overall preference value. Several researchers approach MCRS as a multi-criteria decision making (MCDM) problem, and apply MCDM methods and techniques to implement MCRS systems.^[46] See this chapter^[47] for an extended introduction.

Risk-aware recommender systems[edit]

The majority of existing approaches to recommender systems focus on recommending the most relevant content to users using contextual information, yet do not take into account the risk of disturbing the user with unwanted notifications. It is important to consider the risk of upsetting the user by pushing recommendations in certain circumstances, for instance, during a professional meeting, early morning, or late at night. Therefore, the performance of the recommender system depends in part on the degree to which it has incorporated the risk into the recommendation process. One option to manage this issue is DRARS, a system which models the context-aware recommendation as a bandit problem. This system combines a content-based technique and a contextual bandit algorithm.^[48]

Mobile recommender systems[edit]

Mobile recommender systems make use of internet-accessing smart phones to offer personalized, context-sensitive recommendations. This is a particularly difficult area of research as mobile data is more complex than data that recommender systems often have to deal with. It is heterogeneous, noisy, requires spatial and temporal auto-correlation, and has validation and generality problems.^[49]

There are three factors that could affect the mobile recommender systems and the accuracy of prediction results: the context, the recommendation method and privacy.^[50] Additionally, mobile recommender systems suffer from a transplantation problem – recommendations may not apply in all regions (for instance, it would be unwise to recommend a recipe in an area where all of the ingredients may not be available).

One example of a mobile recommender system are the approaches taken by companies such as Uber and Lyft to generate driving routes for taxi drivers in a city.^[49] This system uses GPS data of the routes that taxi drivers take while working, which includes location (latitude and longitude), time stamps, and operational status (with or without passengers). It uses this data to recommend a list of pickup points along a route, with the goal of optimizing occupancy times and profits.

Mobile recommendation systems have also been successfully built using the "Web of Data" as a source for structured information. A good example of such system is SMARTMUSEUM^[51] The system uses semantic modelling, information retrieval, and machine learning techniques in order to recommend content matching user interests, even when presented with sparse or minimal user data.

Hybrid recommender systems[edit]

Most recommender systems now use a hybrid approach, combining collaborative filtering, content-based filtering, and other approaches . There is no reason why several different techniques of the same type could not be hybridized. Hybrid approaches can be implemented in several ways: by making content-based and collaborative-based predictions separately and then combining them; by adding content-based capabilities to a collaborative-based approach (and vice versa); or by unifying the approaches into one model (see^[22] for a complete review of recommender systems). Several studies that empirically compare the performance of the hybrid with the pure collaborative and content-based methods and demonstrated that the hybrid methods can provide more accurate recommendations than pure approaches. These methods can also be used to overcome some of the common problems in recommender systems such as cold start and the sparsity problem, as well as the knowledge engineering bottleneck in knowledge-based approaches.^[52]

Netflix is a good example of the use of hybrid recommender systems.^[53] The website makes recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering).

Some hybridization techniques include:

• Weighted: Combining the score of different recommendation components numerically.
• Switching: Choosing among recommendation components and applying the selected one.
• Mixed: Recommendations from different recommenders are presented together to give the recommendation.
• Feature Combination: Features derived from different knowledge sources are combined together and given to a single recommendation algorithm.
• Feature Augmentation: Computing a feature or set of features, which is then part of the input to the next technique.
• Cascade: Recommenders are given strict priority, with the lower priority ones breaking ties in the scoring of the higher ones.
• Meta-level: One recommendation technique is applied and produces some sort of model, which is then the input used by the next technique.^[54]

The Netflix Prize[edit]

One of the events that energized research in recommender systems was the Netflix Prize. From to , Netflix sponsored a competition, offering a grand prize of $1,, to the team that could take an offered dataset of over million movie ratings and return recommendations that were 10% more accurate than those offered by the company's existing recommender system. This competition energized the search for new and more accurate algorithms. On 21 September , the grand prize of US$1,, was given to the BellKor's Pragmatic Chaos team using tiebreaking rules.^[55]

The most accurate algorithm in used an ensemble method of different algorithmic approaches, blended into a single prediction. As stated by the winners, Bell et al.:^[56]

Predictive accuracy is substantially improved when blending multiple predictors. Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique. Consequently, our solution is an ensemble of many methods.

Many benefits accrued to the web due to the Netflix project. Some teams have taken their technology and applied it to other markets. Some members from the team that finished second place founded Gravity R&D, a recommendation engine that's active in the RecSys community.^[55][57] 4-Tell, Inc. created a Netflix project–derived solution for ecommerce websites.

A number of privacy issues arose around the dataset offered by Netflix for the Netflix Prize competition. Although the data sets were anonymized in order to preserve customer privacy, in two researchers from the University of Texas were able to identify individual users by matching the data sets with film ratings on the Internet Movie Database.^[58] As a result, in December , an anonymous Netflix user sued Netflix in Doe v. Netflix, alleging that Netflix had violated United States fair trade laws and the Video Privacy Protection Act by releasing the datasets.^[59] This, as well as concerns from the Federal Trade Commission, led to the cancellation of a second Netflix Prize competition in ^[60]

Performance measures[edit]

Evaluation is important in assessing the effectiveness of recommendation algorithms. To measure the effectiveness of recommender systems, and compare different approaches, three types of evaluations are available: user studies, online evaluations (A/B tests), and offline evaluations.^[24]

The commonly used metrics are the mean squared error and root mean squared error, the latter having been used in the Netflix Prize. The information retrieval metrics such as precision and recall or DCG are useful to assess the quality of a recommendation method. Diversity, novelty, and coverage are also considered as important aspects in evaluation.^[61] However, many of the classic evaluation measures are highly criticized.^[62]

User studies are rather small scale. A few dozens or hundreds of users are presented recommendations created by different recommendation approaches, and then the users judge which recommendations are best. In A/B tests, recommendations are shown to typically thousands of users of a real product, and the recommender system randomly picks at least two different recommendation approaches to generate recommendations. The effectiveness is measured with implicit measures of effectiveness such as conversion rate or click-through rate. Offline evaluations are based on historic data, e.g. a dataset that contains information about how users previously rated movies.^[63]

The effectiveness of recommendation approaches is then measured based on how well a recommendation approach can predict the users' ratings in the dataset. While a rating is an explicit expression of whether a user liked a movie, such information is not available in all domains. For instance, in the domain of citation recommender systems, users typically do not rate a citation or recommended article. In such cases, offline evaluations may use implicit measures of effectiveness. For instance, it may be assumed that a recommender system is effective that is able to recommend as many articles as possible that are contained in a research article's reference list. However, this kind of offline evaluations is seen critical by many researchers.^{[64][65][66][24]} For instance, it has been shown that results of offline evaluations have low correlation with results from user studies or A/B tests.^[66][67] A dataset popular for offline evaluation has been shown to contain duplicate data and thus to lead to wrong conclusions in the evaluation of algorithms.^[68] Often, results of so-called offline evaluations do not correlate with actually assessed user-satisfaction.^[69] This is probably because offline training is highly biased toward the highly reachable items, and offline testing data is highly influenced by the outputs of the online recommendation module.^[64] Researchers have concluded that the results of offline evaluations should be viewed critically.

Beyond accuracy[edit]

Typically, research on recommender systems is concerned about finding the most accurate recommendation algorithms. However, there are a number of factors that are also important.

• Diversity – Users tend to be more satisfied with recommendations when there is a higher intra-list diversity, e.g. items from different artists.^[70]
• Recommender persistence – In some situations, it is more effective to re-show recommendations,^[71] or let users re-rate items,^[72] than showing new items. There are several reasons for this. Users may ignore items when they are shown for the first time, for instance, because they had no time to inspect the recommendations carefully.
• Privacy – Recommender systems usually have to deal with privacy concerns^[73] because users have to reveal sensitive information. Building user profiles using collaborative filtering can be problematic from a privacy point of view. Many European countries have a strong culture of data privacy, and every attempt to introduce any level of user profiling can result in a negative customer response. Much research has been conducted on ongoing privacy issues in this space. The Netflix Prize is particularly notable for the detailed personal information released in its dataset. Ramakrishnan et al. have conducted an extensive overview of the trade-offs between personalization and privacy and found that the combination of weak ties (an unexpected connection that provides serendipitous recommendations) and other data sources can be used to uncover identities of users in an anonymized dataset.^[74]

• User demographics – Beel et al. found that user demographics may influence how satisfied users are with recommendations.^[75] In their paper they show that elderly users tend to be more interested in recommendations than younger users.
• Robustness – When users can participate in the recommender system, the issue of fraud must be addressed.^[76]
• Serendipity – Serendipity is a measure of "how surprising the recommendations are".^[77] For instance, a recommender system that recommends milk to a customer in a grocery store might be perfectly accurate, but it is not a good recommendation because it is an obvious item for the customer to buy.
• Trust – A recommender system is of little value for a user if the user does not trust the system.^[78] Trust can be built by a recommender system by explaining how it generates recommendations, and why it recommends an item.
• Labelling – User satisfaction with recommendations may be influenced by the labeling of the recommendations.^[79] For instance, in the cited study click-through rate (CTR) for recommendations labeled as "Sponsored" were lower (CTR=%) than CTR for identical recommendations labeled as "Organic" (CTR=%). Recommendations with no label performed best (CTR=%) in that study.

Reproducibility crisis[edit]

The field of recommender systems has been impacted by the replication crisis as well. A systematic analysis of publications applying deep learning or neural methods to the top-k recommendation problem, published in top conferences (SIGIR, KDD, WWW, RecSys), has shown that on average less than 40% of articles are reproducible, with as little as 14% in some conferences. Overall the study identifies 18 articles, only 7 of them could be reproduced and 6 of them could be outperformed by much older and simpler properly tuned baselines. The article also highlights a number of potential problems in today's research scholarship and calls for improved scientific practices in that area.^[80] Similar issues have been spotted also in sequence-aware recommender systems.^[81] Previous research was also found had little impact on the practical application of recommender systems. By , Ekstrand, Konstan, et al. criticized that "it is currently difficult to reproduce and extend recommender systems research results,” and that evaluations are “not handled consistently".^[82] Konstan and Adomavicius conclude that "the Recommender Systems research community is facing a crisis where a significant number of papers present results that contribute little to collective knowledge […] often because the research lacks the […] evaluation to be properly judged and, hence, to provide meaningful contributions."^[83] As a consequence, much research about recommender systems can be considered as not reproducible.^[84] Hence, operators of recommender systems find little guidance in the current research for answering the question, which recommendation approaches to use in a recommender systems. Said & Bellogín conducted a study of papers published in the field, as well as benchmarked some of the most popular frameworks for recommendation and found large inconsistencies in results, even when the same algorithms and data sets were used.^[85] Some researchers demonstrated that minor variations in the recommendation algorithms or scenarios led to strong changes in the effectiveness of a recommender system. They conclude that seven actions are necessary to improve the current situation:^[84] "(1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research."

See also[edit]

References[edit]