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About this book

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories:

Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.

Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored.

Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed.

In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications.

Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Table of Contents

Chapter 1. An Introduction to Recommender Systems

The increasing importance of the Web as a medium for electronic and business transactions has served as a driving force for the development of recommender systems technology. An important catalyst in this regard is the ease with which the Web enables users to provide feedback about their likes or dislikes. For example, consider a scenario of a content provider such as Netflix. In such cases, users are able to easily provide feedback with a simple click of a mouse. A typical methodology to provide feedback is in the form of ratings, in which users select numerical values from a specific evaluation system (e.g., five-star rating system) that specify their likes and dislikes of various items.
Charu C. Aggarwal

Chapter 2. Neighborhood-Based Collaborative Filtering

Neighborhood-based collaborative filtering algorithms, also referred to as memory-based algorithms, were among the earliest algorithms developed for collaborative filtering. These algorithms are based on the fact that similar users display similar patterns of rating behavior and similar items receive similar ratings. There are two primary types of neighborhood-based algorithms:
Charu C. Aggarwal

Chapter 3. Model-Based Collaborative Filtering

The neighborhood-based methods of the previous chapter can be viewed as generalizations of k-nearest neighbor classifiers, which are commonly used in machine learning.
Charu C. Aggarwal

Chapter 4. Content-Based Recommender Systems

The collaborative systems discussed in the previous chapters use the correlations in the ratings patterns across users to make recommendations. On the other hand, these methods do not use item attributes for computing predictions. This would seem rather wasteful; after all, if John likes the futuristic science fiction movie Terminator, then there is a very good chance that he might like a movie from a similar genre, such as Aliens. In such cases, the ratings of other users may not be required to make meaningful recommendations.
Charu C. Aggarwal

Chapter 5. Knowledge-Based Recommender Systems

Both content-based and collaborative systems require a significant amount of data about past buying and rating experiences. For example, collaborative systems require a reasonably well populated ratings matrix to make future recommendations. In cases where the amount of available data is limited, the recommendations are either poor, or they lack full coverage over the entire spectrum of user-item combinations.
Charu C. Aggarwal

Chapter 6. Ensemble-Based and Hybrid Recommender Systems

In the previous chapters, we discussed three different classes of recommendation methods. Collaborative methods use the ratings of a community of users in order to make recommendations, whereas content-based methods use the ratings of a single user in conjunction with attribute-centric item descriptions to make recommendations.
Charu C. Aggarwal

Chapter 7. Evaluating Recommender Systems

The evaluation of collaborative filtering shares a number of similarities with that of classification. This similarity is due to the fact that collaborative filtering can be viewed as a generalization of the classification and regression modeling problem (cf. section 1.​3.​1.​3 of Chapter 1).
Charu C. Aggarwal

Chapter 8. Context-Sensitive Recommender Systems

For me context is the key – from that comes the understanding
Charu C. Aggarwal

Chapter 9. Time- and Location-Sensitive Recommender Systems

In many real scenarios, the buying and rating behaviors of customers are associated with temporal information. For example, the ratings in the Netflix Prize data set are associated with a “GradeDate” variable, and it was eventually shown [310] how the temporal component could be used to improve the rating predictions.
Charu C. Aggarwal

Chapter 10. Structural Recommendations in Networks

The growth of various Web-enabled networks has enabled numerous models of recommendation. For example, the Web itself is a large and distributed repository of data, and a search engine such as Google can be considered a keyword-centric variation of the notion of recommendation. In fact, a major discourse in the recommendation literature is to distinguish between the notions of search and recommendations. While search technologies also recommend content to users, the results are often not personalized to the user at hand. This lack of personalization has traditionally been the case because of the historical difficulty in tracking large numbers of Web users. However, in recent years, many personalized notions of search have arisen, where the Web pages recommended to users are based on personal interests. Many search engine providers, such as Google, now provide the ability to determine personalized results. This problem is exactly equivalent to that of ranking nodes in networks with the use of personalized preferences.
Charu C. Aggarwal

Chapter 11. Social and Trust-Centric Recommender Systems

With increasing access to social information about users, merchants can directly incorporate social context in collaborative filtering algorithms. Although some of these methods are discussed in Chapter 10, the focus of this chapter is primarily on recommending nodes and links in network settings. Social context is a much broader concept, not only including social (network) links, but also various types of side information, such as tags or folksonomies. Furthermore, the social context can also be understood in a network-agnostic way, as a special case of context-sensitive recommender systems (cf. Chapter 8). The social setting results in a number of human-centric factors, such as trust. When users are aware of the identity of the actors who participate in the feedback process, the trust factor plays an important role. Therefore, the material in this chapter is closely related to that in Chapter 10, but nevertheless it is distinct enough a merit a separate chapter in its own right. In particular, we will study the following aspects of social context in recommender systems:
Charu C. Aggarwal

Chapter 12. Attack-Resistant Recommender Systems

The input to recommender systems is typically provided through open platforms. Almost anyone can register and submit a review at sites such as Amazon.com and Epinions.com. Like any other data-mining system, the effectiveness of a recommender system depends almost exclusively on the quality of the data available to it. Unfortunately, there are significant motivations for participants to submit incorrect feedback about items for personal gain or for malicious reasons:
Charu C. Aggarwal

Chapter 13. Advanced Topics in Recommender Systems

Recommender systems are often used in a number of specialized settings that are not covered in previous chapters of this book. In many cases, the recommendations are performed in settings where there might be multiple users or multiple evaluation criteria. For example, consider a scenario where a group of tourists wish to take a vacation together. Therefore, they may want to obtain recommendations that match the overall interests of the various members in the group. In other scenarios, users may use multiple criteria to provide ratings to items. These variations in the problem formulation can sometimes make the prediction problem more challenging. In particular, we will study the following advanced variations of recommender systems in this chapter:
Charu C. Aggarwal
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