Recommendation systems: Principles, methods and evaluation

Abstract On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.

[1]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[2]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[3]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[4]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[5]  Ali Mamat,et al.  WebPUM: A Web-based recommendation system to predict user future movements , 2010, Expert Syst. Appl..

[6]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[7]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[8]  Sandra Clara Gadanho,et al.  Addressing uncertainty in implicit preferences , 2007, RecSys '07.

[9]  Kamal Kant Bharadwaj,et al.  Fuzzy-genetic approach to recommender systems based on a novel hybrid user model , 2008, Expert Syst. Appl..

[10]  Ioannis Konstas,et al.  On social networks and collaborative recommendation , 2009, SIGIR.

[11]  Michael J. Pazzani,et al.  User Modeling for Adaptive News Access , 2000, User Modeling and User-Adapted Interaction.

[12]  David McSherry,et al.  Explaining the Pros and Cons of Conclusions in CBR , 2004, ECCBR.

[13]  David G. Stork,et al.  Pattern Classification , 1973 .

[14]  Dunja Mladenic,et al.  Web Mining: From Web to Semantic Web , 2004, Lecture Notes in Computer Science.

[15]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[16]  Murat Göksedef,et al.  Combination of Web page recommender systems , 2010, Expert Syst. Appl..

[17]  Tong Zhang,et al.  Recommender systems using linear classifiers , 2002 .

[18]  Kristian J. Hammond,et al.  The FindMe Approach to Assisted Browsing , 1997, IEEE Expert.

[19]  Ingoo Han,et al.  Detection of the customer time-variant pattern for improving recommender systems , 2005, Expert Syst. Appl..

[20]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[21]  Thore Graepel,et al.  Matchbox: large scale online bayesian recommendations , 2009, WWW '09.

[22]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[23]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[24]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[25]  Ahmad M. Ahmad Wasfi.  Collecting user access patterns for building user profiles and collaborative filtering , 1998, IUI '99.

[26]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[27]  Jie Lu,et al.  A trust-semantic fusion-based recommendation approach for e-business applications , 2012, Decis. Support Syst..

[28]  Bingyong Tang,et al.  Recommender System in E-commerce , 2012, ICEE 2012.

[29]  Wei-Ying Ma,et al.  Block-level link analysis , 2004, SIGIR '04.

[30]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[31]  Bradley N. Miller,et al.  Applying Collaborative Filtering to Usenet News , 1997 .

[32]  Oskar Dressler,et al.  Künstliche Intelligenz? , 1986, FIFF Jahrestagung.

[33]  Bradley N. Miller,et al.  Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system , 1998, CSCW '98.

[34]  Young Park,et al.  A time-based approach to effective recommender systems using implicit feedback , 2008, Expert Syst. Appl..

[35]  Lars Schmidt-Thieme,et al.  Taxonomy-driven computation of product recommendations , 2004, CIKM '04.

[36]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[37]  Ivan Koychev,et al.  Learning User Interests through Positive Examples Using Content Analysis and Collaborative Filtering , 2001 .

[38]  Dimitris Plexousakis,et al.  Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents , 2004, Eng. Appl. Artif. Intell..

[39]  Nicolae Tomai,et al.  Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications , 2010, ICWE Workshops.

[40]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[41]  Christian Posse,et al.  Bayesian Mixed-Effects Models for Recommender Systems , 1999 .

[42]  Andrew Jennings,et al.  A Personal News Service Based on a User Model Neural Network , 1992 .

[43]  Ahmet Arslan,et al.  A collaborative filtering method based on artificial immune network , 2009, Expert Syst. Appl..

[44]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[45]  Adam Prügel-Bennett,et al.  A Scalable, Accurate Hybrid Recommender System , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[46]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[47]  Jürgen Buder,et al.  Learning with personalized recommender systems: A psychological view , 2012, Comput. Hum. Behav..

[48]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[49]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[50]  P. Moradi,et al.  A novel collaborative filtering model based on combination of correlation method with matrix completion technique , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[51]  G. James Blaine,et al.  Continuous Monitoring of Physiologic Variables with a Dedicated Minicomputer , 1975, Computer.

[52]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[53]  Boon Chong Lim,et al.  Word-of-mouth: The use of source expertise in the evaluation of familiar and unfamiliar brands , 2014 .

[54]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[55]  Rong Hu,et al.  Acceptance issues of personality-based recommender systems , 2009, RecSys '09.

[56]  Òscar Celma,et al.  Foafing the Music: Bridging the Semantic Gap in Music Recommendation , 2006, SEMWEB.

[57]  Evaggelia Pitoura,et al.  Search result diversification , 2010, SGMD.

[58]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[59]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[60]  Douglas W. Oard,et al.  Implicit Feedback for Recommender Systems , 1998 .

[61]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[62]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.

[63]  Barry Smyth,et al.  PTV: Intelligent Personalised TV Guides , 2000, AAAI/IAAI.

[64]  Peter Brusilovsky,et al.  Social networks and interest similarity: the case of CiteULike , 2010, HT '10.

[65]  Gediminas Adomavicius,et al.  Impact of data characteristics on recommender systems performance , 2012, TMIS.

[66]  Andrea Montanari,et al.  Matrix completion from a few entries , 2009, ISIT.

[67]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..

[68]  Yoon Ho Cho,et al.  A personalized recommender system based on web usage mining and decision tree induction , 2002, Expert Syst. Appl..

[69]  Urszula Kuzelewska.  Advantages of Information Granulation in Clustering Algorithms , 2011, ICAART.

[70]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[71]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[72]  Domonkos Tikk,et al.  Investigation of Various Matrix Factorization Methods for Large Recommender Systems , 2008, ICDM Workshops.

[73]  Chenguang Pan,et al.  Research paper recommendation with topic analysis , 2010, 2010 International Conference On Computer Design and Applications.

[74]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[75]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[76]  John Riedl,et al.  Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems , 2006, ETRICS.

[77]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[78]  Long-Sheng Chen,et al.  Developing recommender systems with the consideration of product profitability for sellers , 2008, Inf. Sci..

[79]  Xin Jin,et al.  Semantically Enhanced Collaborative Filtering on the Web , 2003, EWMF.

[80]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[81]  Bamshad Mobasher.  Recommender Systems , 2007, Künstliche Intell..

[82]  Michael J. Pazzani,et al.  A hybrid user model for news story classification , 1999 .

[83]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[84]  Bradley N. Miller,et al.  Social Information Filtering : Algorithms for Automating “ Word of Mouth , ” , 2017 .

[85]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[86]  Sean M. McNee,et al.  Getting to know you: learning new user preferences in recommender systems , 2002, IUI '02.

[87]  Ram D. Gopal,et al.  Empirical Analysis of the Impact of Recommender Systems on Sales , 2010, J. Manag. Inf. Syst..

[88]  Barry Smyth,et al.  A personalised TV listings service for the digital TV age , 2000, Knowl. Based Syst..

[89]  SongJie Gong,et al.  A personalized recommendation system combining case-based reasoning and user-based collaborative filtering , 2009, 2009 Chinese Control and Decision Conference.

[90]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[91]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[92]  Ralph Bergmann,et al.  WEBSELL: Intelligent Sales Assistants for the World Wide Web , 2001, Künstliche Intell..