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General Introduction to Recommender Systems

1.1. Putting it into perspective

Before the emergence of modern information systems, individuals developed the habit of recommending products or services through "word of mouth", sharing certain social or cultural affinities [OBR 77, SHA 95]. This approach, which can be qualified as social, pursued the principle of sharing an individual experience with others, in areas, at first, as wide as culture or handicraft and then industry. Beyond the reputation tied to the intrinsic quality of a product, there were assessments that emerged through the prism of sociocultural mediums which also improved products and services.

Today, offers - whether information or products - are increasing day-by-day, proposed on the Internet. Beyond a certain threshold, too much information can lead to a deterioration of the quality of the message, which we refer to as information overload [LEV 98, CHE 09]. For the end user in search of information, it is of interest for the system to carry out preprocessing in order to filter the least important elements, in line with their expectations. The development of automated recommender systems (RecSys) is therefore a foreseeable phenomenon for contributing toward resolving the problem of information overload, valuing content and focusing attention on the user in such a context of overabundance.

The first recommender systems, using "collaborative filtering", had the aim of using the volume of community evaluations in order to propose personalized cultural advice, based on evaluation statistics and the correlation of user profiles [RES 94].

As early as 2000, Burke remarked that many commercial websites such as Amazon or even eBay had understood the purpose of contextualizing peripheral hyperlink offers consulted by the user [BUR 00]. Commercial search engines have even created related products such as "Google AdSense" in order to optimize advertising profits by taking advantage of recommendations based on the contents of queries, or even e-mails1. The principle is simply to propose private advertisers to provide hyperlinks directed toward their website in the margin of content selected by the user. This second method is called the content-based method.

With the arrival of social networks, be they in the public or professional spheres, sharing and the evaluation of content have become a mass worldwide phenomenon. As a result of this unprecedented generation of data, mercantile diversions are common and have led AFNOR2 to propose standards for controlling the phenomenon [AFN 13].

1.2. An interdisciplinary subject

The first notable papers confirming recommender systems as a dedicated area of study and research involved computer science specialists as well as economists invested in the emerging development of e-commerce. The issue of information systems unified them; it has become a decisive factor in the decision-making of organizations. Thus, the precursory paper by Paul Resnick (AT&T) and Hal R. Varian (Berkeley School of Information Management) in 1997 focused on the functional analysis of five precursory recommender systems by mostly concentrating on the business model and risks of corruption of such systems [RES 97]. In 2000, Robin Burke, a researcher in computer science, prioritized mentioning the emergence of large catalogs and the required assistance for the consumer in making their choices; his articles focused on the design of algorithms and their performance [BUR 00]. E-commerce and recommendation algorithms were originally linked.

The data in Table 1.1, collected by consulting the digital library of publications of the Association for Computing Machinery (ACM) about the thematic area of "Recommender System" in the titles of articles, show the increase in interest in this subject over the last 5 years. This count remains partial compared to the set of articles published by other publishers on this subject over the same period. The growth of information as well as the major development of online commercial platforms explains for the most part the stakes associated with the issue; its development goes hand in hand with the optimization of information systems and the needs of e-marketing.

Table 1.1. Increase in the number of articles dedicated to recommender systems in the library of the ACM (http://dl.acm.org/)

The international conference on recommender systems (RecSys) was held in 2007 by the ACM and gathered many RecSys specialists. The 8th meeting of the conference will be held in Silicon Valley at the end of 20143.

The literature shows that the computer approach is focused on the performance of algorithms, their robustness, the design and comparison of systems based on semantic, social as well as hybrid data. The proposed evaluation is often centered around the interaction with the technical system, but does not take into account the more qualitative approaches centered around the user. The computer approach also takes into consideration questions related to the transparency, clarification, trust and measurement of recommendation diversity. The ongoing renewal mainly includes combinations with other technologies: notably the Web of data, Big Data and automated sentiment analysis.

E-commerce approaches are mostly focused on new techniques which can direct potential clients to targeted products and services. The combinations of different types of recommendation have been tested in fields such as tourism and cultural industries (selling of books, music, on-demand video). Recommender systems are considered to be marketing tools and technologies specific to "business intelligence", a set of methods and technologies which transform data into useful information for decision-making in industry.

From the point of view of information science, identified works are more recent; they highlight the use of such systems for developing discovery functions in digital libraries and library catalogs [WAK 12]. Qualitative evaluations of recommendations, the perspective of users and psychological factors are all perspectives of analysis which are specific to recommender systems and which open up new areas of research in this field with the help of abundant literature on techniques and algorithms. Several conferences are focused, however, on the user experience with these recommender systems by assessing their acceptance or rejection placed in this context. It is notably the aspects of visualization, clarification, transparency, trust, and help in decision-making which are the objects of investigations by researchers from various subject areas4.

1.3. The fundamentals of algorithms

Here, we introduce the foundations of recommendation systems, models and methods to provide a better context for the later chapters. This conceptual appropriation is intended to be neutral and factual; it will pave the way for the presentation of more involved points of view in the rest of this book.

1.3.1. Collaborative filtering

Historically, the first system proposed was based on collaborative filtering. This method assumes an authentication of users on the content management platform and, of course, personal input. Once a document has been proposed to the user by the system on the basis of criteria researched during the creation of the profile and/or the use of an additional internal search engine by the user, the latter will propose the possibility of attributing a rating to it. This rating can be an intrinsic assessment of the document, or an assessment of the relevance to the context of the search and its main intentions.

This rating will be preserved within the system to be reused. According to the "memory-based" or heuristic collaborative filtering, ratings can help predict the assessment of a user a of an item based on that of another user ß, having regularly rated in a similar way. In order to determine which user ß is most similar to user a, the Pearson correlation is often used [RES 94]. This method is also referred to as "Word of Mouth" [SHA 95] or "People-to-People Correlation" [SCH 99].

Let r be the Pearson correlation coefficient which in our case compares ratings, from 0 to 10, of 2 users for a collection of items. We note that this function is integrated into modern spreadsheets5. The correlation will be weak if the coefficient is less than 0.5 and strong if it tends toward 1.

Pearson correlation:

[1.1]

Example of the computation of the similarity between users having rated a set of items. Table 1.2 displays a collection of user assessments for certain items.

Table 1.2. Example of a sample of ratings

Table 1.3 displays the correlation coefficients computed two by two for the collection. The values in bold show strongly correlated users.

Table 1.3. Similarity of users based on their Pearson correlation

In the example, for the values presented in Table 1.2, the results displayed in Table 1.3 show that each user can benefit from the assessments of at least one other user with a similar profile to theirs...