Recommender Systems: Introduction and Challenges
Published 1 January 2015
Francesco Ricci⋆, Lior Rokach, Bracha Shapira
Citations814
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TL;DR
This introductory chapter briefly discusses basic RS ideas and concepts and aims to delineate, in a coherent and structured way, the chapters included in this handbook.
Abstract
Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular 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. Additionally, we aim to help the reader navigate the rich and detailed content that this handbook offers.
Keywords
Computer ScienceDecision Sciences
ComputerMatrix Factorization Techniques for Recommender Systems
11,675 Citations2009Yehuda Koren, Robert Bell +1 more
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
IEEE Transactions on Knowledge and Data EngineeringToward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
10,115 Citations2005Gediminas Adomavičius, Alexander Tuzhilin
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
ACM Transactions on Information SystemsEvaluating collaborative filtering recommender systems
5,762 Citations2004Jonathan L. Herlocker, Joseph A. Konstan +2 more
The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
IEEE Internet ComputingAmazon.com recommendations: item-to-item collaborative filtering
5,369 Citations2003Greg Linden, Brent Smith +1 more
This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
Communications of the ACMUsing collaborative filtering to weave an information tapestry
4,040 Citations1992David Theo Goldberg, David M. Nichols +2 more
Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser.
Communications of the ACMRecommender systems
3,685 Citations1997Paul Resnick, Hal R. Varian
This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.
Knowledge-Based SystemsRecommender systems survey
3,100 Citations2013Jesús Bobadilla, Fernando Ortega +2 more
An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Communications of the ACMFab
2,927 Citations1997Marko Balabanović, Yoav Shoham
It is explained how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither, and how the particular design of the Fab architecture brings two additional benefits.
Social information filtering
2,800 Citations1995Upendra Shardanand, Pattie Maes
The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.
User Modeling and User-Adapted InteractionMethods and techniques of adaptive hypermedia
2,031 Citations1996Peter Brusilovsky
This paper is a review of existing work on adaptive hypermedia and introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.
Lecture notes in computer scienceCollaborative Filtering Recommender Systems
2,027 Citations2007J. Ben Schafer, Dan Frankowski +2 more
This chapter introduces the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings.
Content-based Recommender Systems: State of the Art and Trends
1,807 Citations2010Pasquale Lops, Marco de Gemmis +1 more
The role of User Generated Content is described 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.
Explaining collaborative filtering recommendations
1,698 Citations2000Jonathan L. Herlocker, Joseph A. Konstan +1 more
This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Data Mining and Knowledge DiscoveryE-Commerce Recommendation Applications
1,518 Citations2001J. Ben Schafer, Joseph A. Konstan +1 more
An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.
Lecture notes in computer scienceHybrid Web Recommender Systems
1,125 Citations2007Robin Burke
This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies and finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
Recommender Systems: An Introduction
1,113 Citations2010Dietmar Jannach, Markus Zanker +2 more
Being accurate is not enough
1,055 Citations2006Sean M. McNee, John Riedl +1 more
This paper proposes informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies, and proposes new user-centric directions for evaluating recommender systems.
Physics ReportsRecommender systems
951 Citations2012Linyuan Lü, Matúš Medo +4 more
This article compares and evaluate available algorithms and examine their roles in the future developments and emphasizes that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.
Cambridge University Press eBooksRecommender Systems
844 Citations2010Dietmar Jannach, Markus Zanker +2 more
User Modeling and User-Adapted InteractionExplaining the user experience of recommender systems
783 Citations2012Bart P. Knijnenburg, Martijn C. Willemsen +3 more
This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively).
User Modeling and User-Adapted InteractionRecommender systems: from algorithms to user experience
744 Citations2012Joseph A. Konstan, John Riedl
It is argued that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and additional measures that have proven effective are suggested.
User Modeling and User-Adapted InteractionUser Modeling in Human–Computer Interaction
724 Citations2001Gerhard Fischer
Expert Systems with ApplicationsA literature review and classification of recommender systems research
659 Citations2012Deuk Hee Park, Hyea Kyeong Kim +2 more
This research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender system research.
Artificial Intelligence ReviewA Taxonomy of Recommender Agents on the Internet
637 Citations2003Miquel Montaner, Beatriz López +1 more
A state-of-the-art taxonomy of intelligent recommender agents on the Internet and a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.
Expert Systems with ApplicationsIntelligent tourism recommender systems: A survey
474 Citations2014Joan Borràs, Antonio Moreno +1 more
A detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques.
Is seeing believing?
464 Citations2003Dan Cosley, Shyong K. Lam +3 more
Two aspects of recommender system interfaces that may affect users' opinions are studied: the rating scale and the display of predictions at the time users rate items, finding that users rate fairly consistently across rating scales.
Comparing Recommendations Made by Online Systems and Friends.
428 Citations2001Rashmi Sinha, Kirsten Swearingen
The hypothesis was that friends would make superior recommendations since they know the user well, and have intimate knowledge of his / her tastes in a number of domains, in contrast to RS, which only have limited, domain-specific knowledge about the users.
Content-based, collaborative recommendation
413 Citations1997Marko Balabanović, Yoav Shoham
Lecture notes in computer scienceGenerating Predictive Movie Recommendations from Trust in Social Networks
399 Citations2006Jennifer Golbeck
This paper presents FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations, and shows that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average.
IEEE SoftwareRecommendation Systems for Software Engineering
382 Citations2009Martin P. Robillard, Robert J. Walker +1 more
The authors provide an overview of recommendation systems for software engineering: what they are, what they can do for developers, and what they might do in the future.
Communications of the ACMPersonalization technologies
341 Citations2005Gediminas Adomavičius, Alexander Tuzhilin
By leveraging customer reactions to personalized products and services, companies continuously improve their personalization processes through an iterative feedback loop resulting in the `virtuous cycle' of personalization.
User Modeling and User-Adapted InteractionCritiquing-based recommenders: survey and emerging trends
277 Citations2011Li Chen, Pearl Pu
A hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions.
Improving recommender systems with adaptive conversational strategies
256 Citations2009Tariq Mahmood, Francesco Ricci⋆
It is shown that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions, and allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques.
The Knowledge Engineering ReviewCase-based recommender systems
248 Citations2005Derek Bridge, Mehmet H. Göker +2 more
A framework in which these systems can be understood is defined, and a selection of papers from the case-based recommender systems literature is reviewed, covering the development of these systems over the last ten years.
Recommendation Systems in Software Engineering
212 Citations2014Martin P. Robillard, Walid Maalej +2 more
This introduction presents an overview of the issues and considerations involved in creating, evaluating, and using RSSEs, and presents a general outlook on the current state of research and development in the field of recommendation systems for highly technical domains.
User Modeling and User-Adapted InteractionMediation of user models for enhanced personalization in recommender systems
207 Citations2007Shlomo Berkovsky, Tsvi Kuflik +1 more
A generic user modeling data representation model is provided, which demonstrates its compatibility with existing recommendation techniques, and allows improving the quality of the recommendations provided to the users in certain conditions.
User perception of differences in recommender algorithms
194 Citations2014Michael D. Ekstrand, F. Maxwell Harper +2 more
It is found that satisfaction is negatively dependent on novelty and positively dependent on diversity in this setting, and that satisfaction predicts the user's final selection of a recommender that they would like to use in the future.
Computer Science ReviewContextual music information retrieval and recommendation: State of the art and challenges
192 Citations2012Marius Kaminskas, Francesco Ricci⋆
This survey illustrates various tools and techniques that can be used for addressing the research challenges posed by context-aware music retrieval and recommendation.
Dataset-driven research for improving recommender systems for learning
171 Citations2011Katrien Verbert, Hendrik Drachsler +4 more
An experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets are presented and implicit relevance data, such as downloads and tags, that can be used to improve the performance of recommendation algorithms are elaborate.
The million song dataset challenge
169 Citations2012Brian McFee, Thierry Bertin-Mahieux +2 more
The Million Song Dataset Challenge is introduced: a large-scale, personalized music recommendation challenge, where the goal is to predict the songs that a user will listen to, given both the user's listening history and full information (including meta-data and content analysis) for all songs.
ACM SIGKDD Explorations NewsletterMining large streams of user data for personalized recommendations
128 Citations2013Xavier Amatriain
An up-to-date overview of the use of data mining approaches for personalization and recommendation at Netflix and the most promising current research avenues and unsolved problems that deserve attention are pinpointed.
ACM Transactions on Interactive Intelligent SystemsHuman Decision Making and Recommender Systems
118 Citations2013Li Chen, Marco de Gemmis +4 more
How the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort is highlighted.
User Modeling and User-Adapted InteractionExperimental evaluation of context-dependent collaborative filtering using item splitting
107 Citations2013Linas Baltrunas, Francesco Ricci⋆
It is shown that Item Splitting can be beneficial and its performance depends on the method used to determine which items to split, and that the benefit of the method is determined by the relevance of the contextual factors that are used to split.
IEEE Intelligent SystemsDiscrete sensor validation with multilevel flow models
106 Citations2002Bengt Öhman
The described sensor validation algorithm monitors a process's active alarms, alerting operators and support personnel to potential false alarms.
IT ProfessionalImproving Social Recommender Systems
98 Citations2009Ofer Arazy, Nanda Kumar +1 more
Recommender systems play a significant role in reducing information overload for people visiting online sites, but their accuracy could be improved by using data from online social networks and electronic communication tools.
Usage-based web recommendations
85 Citations2007Nima Taghipour, Ahmad A. Kardan +1 more
This paper proposes that the reinforcement learning paradigm provides an appropriate model for the recommendation problem, as well as a framework in which the system constantly interacts with the user and learns from her behavior, and how this approach can improve the quality of web recommendations.
Advances in soft computingRecommender System from Personal Social Networks
65 Citations2007David Ben-Shimon, Alexander Tsikinovsky +4 more
A new method for recommender system that employs the users’ social network in order to provide better recommendation for media items such as movies or TV shows is proposed and a new paradigm for incorporating the feedback of the user’s friends is developed.
User Modeling and User-Adapted InteractionCross-representation mediation of user models
40 Citations2008Shlomo Berkovsky, Tsvi Kuflik +1 more
This work discusses and evaluates a cross-representation mediation of UMs from collaborative filtering to content-based recommender systems, and shows that the mediation can be used to improve a content- based recommender system by incrementally mediating collaborative filtering UM data (item ratings) and enriching the availablecontent-based UMs.
Journal of the American Podiatric Medical AssociationLearning Probabilistic User Models
39 Citations1998Daniel Billsus
Recommender Systems: Models and Techniques
28 Citations2014Francesco Ricci⋆
The system’s prediction of the user’s evaluation for an item and the conditions under which an item is evaluated by a user are described.
Beyond personalization
11 Citations2005Mark van Setten, Sean M. McNee +1 more
This workshop intends to bring recommender systems researchers and practitioners together in order to discuss the current state ofRecommender systems research, both on existing and emerging research topics, and to determine how research in this area should proceed.
Lecture notes in computer scienceAn Acceptance Model of Recommender Systems Based on a Large-Scale Internet Survey
4 Citations2012Hideki Asoh, Chihiro Ono +4 more
This study developed an acceptance model of recommender systems based on a large-scale internet survey using 60 kinds of pseudo-services and found that some users feel uneasiness regarding such information acquisition by systems and have concern over their online privacy.