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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.

Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included.

Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.



Chapter 1. Introduction to Recommender Systems Handbook

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.
Francesco Ricci, Lior Rokach, Bracha Shapira

Basic Techniques


Chapter 2. Data Mining Methods for Recommender Systems

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.
Xavier Amatriain, Alejandro Jaimes*, Nuria Oliver, Josep M. Pujol

Chapter 3. Content-based Recommender Systems: State of the Art and Trends

Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
Pasquale Lops, Marco de Gemmis, Giovanni Semeraro

Chapter 4. A Comprehensive Survey of Neighborhood-based Recommendation Methods

Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.
Christian Desrosiers, George Karypis

Chapter 5. Advances in Collaborative Filtering

The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.
Yehuda Koren, Robert Bell

Chapter 6. Developing Constraint-based Recommenders

Traditional recommendation approaches (content-based filtering [48] and collaborative filtering[40]) are well-suited for the recommendation of quality&taste products such as books, movies, or news. However, especially in the context of products such as cars, computers, appartments, or financial services those approaches are not the best choice (see also Chapter 11). For example, apartments are not bought very frequently which makes it rather infeasible to collect numerous ratings for one specific item (exactly such ratings are required by collaborative recommendation algorithms). Furthermore, users of recommender applications would not be satisfied with recommendations based on years-old item preferences (exactly such preferences would be exploited in this context by content-based filtering algorithms).
Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, Markus Zanker

Chapter 7. Context-Aware Recommender Systems

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.
Gediminas Adomavicius, Alexander Tuzhilin

Applications and Evaluation of RSs


Chapter 8. Evaluating Recommendation Systems

Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recommenders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and finally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the properties that they evaluate.
Guy Shani, Asela Gunawardana

Chapter 9. A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

In this chapter we describe the integration of a recommender system into the production environment of Fastweb, one of the largest European IP Television (IPTV) providers. The recommender system implements both collaborative and content-based techniques, suitable tailored to the specific requirements of an IPTV architecture, such as the limited screen definition, the reduced navigation capabilities, and the strict time constraints. The algorithms are extensively analyzed by means of off-line and on-line tests, showing the effectiveness of the recommender systems: up to 30% of the recommendations are followed by a purchase, with an estimated lift factor (increase in sales) of 15%.
Riccardo Bambini, Paolo Cremonesi, Roberto Turrin

Chapter 10. How to Get the Recommender Out of the Lab?

A personalised system is a complex system made of many interacting parts, from data ingestion to presenting the results to the users. A plethora of methods, tools, algorithms and approaches exist for each piece of such a system: many data and metadata processing methods, many user models, many filtering techniques, many accuracy metrics, many personalisation levels. In addition, a realworld recommender is a piece of an even larger and more complex environment on which there is little control: often the recommender is part of a larger application introducing constraints for the design of the recommender, e.g. the data may not be in a suitable format, or the environment may impose some architectural or privacy constraints. This can make the task of building such a recommender system daunting, and it is easy to make errors. Based on the experience of the authors and the study of other works, this chapter intends to be a guide on the design, implementation and evaluation of personalised systems. It presents the different aspects that must be studied before the design is even started, and how to avoid pitfalls, in a hands-on approach. The chapter presents the main factors to take into account to design a recommender system, and illustrates them through case studies of existing systems to help navigate in the many and complex choices that have to be faced.
Jérome Picault, Myriam Ribière, David Bonnefoy, Kevin Mercer

Chapter 11. Matching Recommendation Technologies and Domains

Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers to identify the recommendation technologies that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system need in order to function, and where does that knowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.
Robin Burke, Maryam Ramezani

Chapter 12. Recommender Systems in Technology Enhanced Learning

Technology enhanced learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This chapter attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.
Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans Hummel, Rob Koper

Interacting with Recommender Systems


Chapter 13. On the Evolution of Critiquing Recommenders

Over the past decade a significant amount of recommender systems research has demonstrated the benefits of conversational architectures that employ critique-based interfacing (e.g., Show me more like item A, but cheaper). The critiquing phenomenon has attracted great interest in line with the growing need for more sophisticated decision/recommendation support systems to assist online users who are overwhelmed by multiple product alternatives. Originally proposed as a powerful yet practical solution to the preference elicitation problem central to many conversational recommenders, critiquing has proved to be a popular topic in a variety of related areas (e.g., group recommendation, mixed-initiative recommendation, adaptive user interfacing, recommendation explanation). This chapter aims to provide a comprehensive, yet concise, source of reference for researchers and practitioners starting out in this area. Specifically, we present a deliberately non-technical overview of the critiquing research which has been covered in recent years.
Lorraine McGinty, James Reilly

Chapter 14. Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations

Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding the influence of source characteristics, which is abundant in the context of humanhuman relationships, can provide an important framework for identifying potential influence factors. This chapter reviews the existing literature on source characteristics in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.
Kyung-Hyan Yoo, Ulrike Gretzel

Chapter 15. Designing and Evaluating Explanations for Recommender Systems

This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”, and suggest guidelines as how to best evaluate this. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing systems, and how these relate to evaluations with live recommender systems. We also discuss how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.
Nava Tintarev, Judith Masthoff

Chapter 16. Usability Guidelines for Product Recommenders Based on Example Critiquing Research

Over the past decade, our group has developed a suite of decision tools based on example critiquing to help users find their preferred products in e-commerce environments. In this chapter, we survey important usability research work relative to example critiquing and summarize the major results by deriving a set of usability guidelines. Our survey is focused on three key interaction activities between the user and the system: the initial preference elicitation process, the preference revision process, and the presentation of the systems recommendation results. To provide a basis for the derivation of the guidelines, we developed a multi-objective framework of three interacting criteria: accuracy, confidence, and effort (ACE). We use this framework to analyze our past work and provide a specific context for each guideline: when the system should maximize its ability to increase users’ decision accuracy, when to increase user confidence, and when to minimize the interaction effort for the users. Due to the general nature of this multi-criteria model, the set of guidelines that we propose can be used to ease the usability engineering process of other recommender systems, especially those used in e-commerce environments. The ACE framework presented here is also the first in the field to evaluate the performance of preference-based recommenders from a user-centric point of view.
Designers can use these guidelines for the implementation of an effective and successful product recommender.
Pearl Pu, Boi Faltings, Li Chen, Jiyong Zhang, Paolo Viappiani

Chapter 17. Map Based Visualization of Product Catalogs

Traditionally, recommender systems present recommendations in ranked lists to the user. In content- and knowledge-based recommender systems, these lists are often sorted on some notion of similarity with a query, ideal product specification, or sample product. However, a lot of information is lost in this way, since two products with the same similarity to a query can differ from this query on a completely different set of product characteristics. When using a two dimensional map based visualization of the recommendations, it is possible to retain part of this information. In the map, we can then position recommendations that are similar to each other in the same area of the map.
Martijn Kagie, Michiel van Wezel, Patrick J.F. Groenen

Recommender Systems and Communities


Chapter 18. Communities, Collaboration, and Recommender Systems in Personalized Web Search

Web search engines are the primary means by which millions of users access information everyday and the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last ten years. However, mainstream search engines continue to deliver largely one-size-fits-all services to their user-base, ultimately limiting the relevance of their result-lists. In this chapter we will explore recent research that is seeking to make Web search a more personal and collaborative experience as we look towards a new breed of more social search engines.
Barry Smyth, Maurice Coyle, Peter Briggs

Chapter 19. Social Tagging Recommender Systems

The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.
Leandro Balby Marinho, Alexandros Nanopoulos, Lars Schmidt-Thieme, Robert Jäschke, Andreas Hotho, Gerd Stumme, Panagiotis Symeonidis

Chapter 20. Trust and Recommendations

Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.
Patricia Victor, Martine De Cock, Chris Cornelis

Chapter 21. Group Recommender Systems: Combining Individual Models

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
Judith Masthoff

Advanced Algorithms


Chapter 22. Aggregation of Preferences in Recommender Systems

This chapter gives an overview of aggregation functions toward their use in recommender systems. Simple aggregation functions such as the arithmetic mean are often employed to aggregate user features, item ratings, measures of similarity, etc., however many other aggregation functions exist which could deliver increased accuracy and flexibility to many systems. We provide definitions of some important families and properties, sophisticated methods of construction, and various examples of aggregation functions in the domain of recommender systems.
Gleb Beliakov, Tomasa Calvo, Simon James

Chapter 23. Active Learning in Recommender Systems

Recommender Systems (RSs) are often assumed to present items to users for one reason – to recommend items a user will likely be interested in. Of course RSs do recommend, but this assumption is biased, with no help of the title, towards the “recommending” the system will do. There is another reason for presenting an item to the user: to learn more about his/her preferences, or his/her likes and dislikes. This is where Active Learning (AL) comes in. Augmenting RSs with AL helps the user become more self-aware of their own likes/dislikes while at the same time providing new information to the system that it can analyze for subsequent recommendations. In essence, applying AL to RSs allows for personalization of the recommending process, a concept that makes sense as recommending is inherently geared towards personalization. This is accomplished by letting the system actively influence which items the user is exposed to (e.g. the items displayed to the user during sign-up orduring regular use), and letting the user explore his/her interests freely.
Neil Rubens, Dain Kaplan, Masashi Sugiyama

Chapter 24. Multi-Criteria Recommender Systems

This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multicriteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recommenders. Then, it focuses on the category of multi-criteria rating recommenders – techniques that provide recommendations by modelling a user’s utility for an item as a vector of ratings along several criteria. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. Finally, the chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.
Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon

Chapter 25. Robust Collaborative Recommendation

Collaborative recommender systems are vulnerable to malicious users who seek to bias their output, causing them to recommend (or not recommend) particular items. This problem has been an active research topic since 2002. Researchers have found that the most widely-studied memory-based algorithms have significant vulnerabilities to attacks that can be fairly easily mounted. This chapter discusses these findings and the responses that have been investigated, especially detection of attack profiles and the implementation of robust recommendation algorithms.
Robin Burke, Michael P. O’Mahony, Neil J. Hurley


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