Bibliography on Data Mining: Recommender Systems

[MGT⁺87]

Thomas W Malone, Kenneth R Grant, Franklyn A Turbak, Stephen A Brobst, and Michael D Cohen. Intelligent information-sharing systems. Communications of the ACM, 30(5):390-402, 1987. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[GNOT92]

David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70, 1992. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[RIS⁺94]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175-186. ACM, 1994. [ bib | find paper in Google Scholar | find paper in Google ]

[SM95]

U. Shardanand and P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. In Conference proceedings on Human factors in computing systems (CHI'95), pages 210-217, Denver, CO, May 1995. ACM Press/Addison-Wesley Publishing Co. [ bib | find paper in Google Scholar | find paper in Google ]

[RV97]

Paul Resnick and Hal R. Varian. Recommender systems. Commun. ACM, 40(3):56-58, 1997. [ bib | find paper in Google Scholar | find paper in Google ]

[KMM⁺97]

Joseph A. Konstan, Bradley N. Miller, David Maltz, JonathanL. Herlocker, Lee R. Gordon, and John Riedl. Grouplens: applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77-87, 1997. [ bib | find paper in Google Scholar | find paper in Google ]

[UF98]

L. Ungar and D. Foster. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park California, 1998. [ bib | find paper in Google Scholar | find paper in Google ]

[BHK98]

John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference, pages 43-52, 1998. [ bib | find paper in Google Scholar | find paper in Google ]

[BP98]

Daniel Billsus and Michael J. Pazzani. Learning collaborative information filters. In ICML '98: Proceedings of the Fifteenth International Conference onMachine Learning, pages 46-54, San Francisco, CA, USA, 1998. Morgan Kaufmann Publishers Inc. [ bib | find paper in Google Scholar | find paper in Google ]

[SKR99]

J. Ben Schafer, Joseph Konstan, and John Riedi. Recommender systems in e-commerce. In EC '99: Proceedings of the 1st ACM conference on Electronic commerce, pages 158-166, New York, NY, USA, 1999. ACM. [ bib | find paper in Google Scholar | find paper in Google ]

[HKBR99]

Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval, pages 230-237, 1999. [ bib | find paper in Google Scholar | find paper in Google ]

[CMS99]

Robert Cooley, Bamshad Mobasher, and Jaidep Srivastava. Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems, 1(1):5-32, 1999. [ bib | find paper in Google Scholar | find paper in Google ]

[VO00]

S. Vucetic and Z. Obradovic. A regression-based approach for scaling-up personalized recommender systems in e-commerce. In ACM WebKDD 2000 Web Mining for E-Commerce Workshop, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[Spi00]

Myra Spiliopoulou. Web usage mining for web site evaluation. Communications of the ACM, 43(8):127-134, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[SPK00]

Ingo Schwab, Wolfgang Pohl, and Ivan Koychev. Learning to recommend from positive evidence. In Proceedings of Intelligent User Interfaces 2000, ACM, pages 241-247, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[SKKR00]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Analysis of recommendation algorithms for e-commerce. In EC '00: Proceedings of the 2nd ACM conference on Electronic commerce, pages 158-167. ACM, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[MDL⁺00]

B. Mobasher, H. Dai, T. Luo, M. Nakagawa, Y. Sun, and J. Wiltshire. Discovery of aggregate usage profiles for web personalization. In ACM WebKDD 2000 Web Mining for E-Commerce Workshop, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[MCS00]

B. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on web usage mining. Communications of the ACM, 43(8):142-151, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[KFV00]

Brendan Kitts, David Freed, and Martin Vrieze. Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities. In KDD '00: Proceedings of the sixth ACM SIGKDD international conferenceon Knowledge discovery and data mining, pages 437-446. ACM, 2000. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[FBH00]

Xiaobin Fu, Jay Budzik, and Kristian J. Hammond. Mining navigation history for recommendation. In IUI '00: Proceedings of the 5th international conference on Intelligentuser interfaces, pages 106-112. ACM, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[Coo00]

Robert Walker Cooley. Web usage mining: Discovery and application of interesting patterns from web data. Ph. d. thesis, Graduate School of the University of Minnesota, University of Minnesota, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[AEK00]

Asim Ansari, Skander Essegaier, and Rajeev Kohli. Internet recommendation systems. Journal of Marketing Research, 37:363-375, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[HKBR00]

Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. Explaining collaborative filtering recommendations. In Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, pages 241-250, December 2000. [ bib | find paper in Google Scholar | find paper in Google ]

[YXEK01]

Kai Yu, Xiaowei Xu, Martin Ester, and Hans-Peter Kriegel. Selecting relevant instances for efficient accurate collaborative filtering. In Proceedings of the 10th CIKM, pages 239-246. ACM Press, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[WI01]

Sholom M. Weiss and Nitin Indurkhya. Lightweight collaborative filtering method for binary-encoded data. In Principles of Data Mining and Knowledge Discovery, volume 2168/2001 of Lecture Notes in Computer Science, pages 484-491. Springer, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[SKK01]

Ingo Schwab, Alfred Kobsa, and Ivan Koychev. Learning user interests through positive examples using content analysis and collaborative filtering. In 30 2001. Internal Memo, GMD, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[SKR01]

J. Ben Schafer, Joseph A. Konstan, and John Riedl. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1/2):115-153, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[SKKR01]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In WWW '01: Proceedings of the 10th international conference on World Wide Web, pages 285-295. ACM, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[Qua01]

André Quadt. Personalisierung im e-commerce. Diplomarbeit, AIFB, Universität Karlsruhe (TH), D-76128 Karlsruhe, Germany, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[PaPD01]

George Prassas, Katherine C. Pramataris andOlga Papaemmanouil, and Georgios J. Doukidis. A recommender system for online shopping based on past customer behaviour. In 14th Bled Electronic Commerce Conference, Bled, Slovenia, June 25-26, 2001, pages 766-782, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[MDLN01]

B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Effective personalization based on association rule discovery from web usage data. In Proceedings of the ACM Workshop on Web Information and Data Management (WIDM01), Atlanta, Georgia, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[LAK⁺01]

Richard D. Lawrence, George S. Almasi, Vladimir Kotlyar, Marisa S. Viveros, and Sastry Duri. Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery, 5(1/2):11-32, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[Kar01]

George Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM '01: Proceedings of the tenth international conference on Informationand knowledge management, pages 247-254. ACM, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[HCRK01]

David Heckerman, David Maxwell Chickering, Christopher Meekand Robert Rounthwaite, and Carl Kadie. Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res., 1:49-75, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[GRGP01]

Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133-151, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[AT01]

Gediminas Adomavicius and Alexander Tuzhilin. Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5(1/2):33-58, 2001. [ bib | find paper in Google Scholar | find paper in Google ]

[TK02]

Pang-Nin Tan and Vipin Kumar. Discovery of web robot sessions based on their navigational patterns. Data Mining and Knowledge Discovery, 6:9-35, 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[SKKR02]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the Fifth International Conference on Computer andInformation Technology, 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[MDTL02]

Bamshad Mobasher, Honghua Dai, and Miki Nakagawa Tao Luo. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 6:61-82, 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[MN02]

Andreas Mild and Martin Natter. Collaborative filtering or regression models for internet recommendation systems? Journal of Targeting, Measurement and Analysis for Marketing, 10(4):304-313, 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[LAR02]

Weiyang Lin, Sergio A. Alvarez, and Carolina Ruiz. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6:83-105, 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[JAA02]

Alipio Jorge, Mario Amado Alves, and Paulo Azevedo. Recommendation with association rules: A web mining application. In Conference on Data Mining and Warehouses (SiKDD 2002), October 15,2002, Ljubljana, Slovenia, 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[Bur02]

Robin Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-370, 2002. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[GSHJ02]

Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In R. Kohavi, B.M. Masand, M. Spiliopoulou, and J. Srivastava, editors, WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers, Lecture Notes in Computer Science LNAI 2356, pages 25-47. Springer-Verlag, July 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[BMSN02]

Bettina Berendt, Bamshad Mobasher, Myra Spiliopoulou, and Miki Nakagawa. The impact of site structure and user environment on session reconstruction in web usage analysis. In Proceedings of the 4th WebKDD 2002 Workshop, at the ACM-SIGKDD Conference n Knowledge Discovery in Databases (KDD'2002), Edmonton, Alberta, Canada, July 2002. [ bib | find paper in Google Scholar | find paper in Google ]

[VM03]

Emmanouil Vozalis and Konstantinos G. Margaritis. Analysis of recommender systems' algorithms. In Proceedings of the sixth Hellenic European conference on computermathematics and its applications (HERCMA 2003), Athens, Greece., 2003. [ bib | find paper in Google Scholar | find paper in Google ]

[MLL03]

Miquel Montaner, Beatriz López, and Josep Lluís De LaRosa. A taxonomy of recommender agents on theinternet. Artificial Intelligence Review, 19(4):285-330, 2003. [ bib | find paper in Google Scholar | find paper in Google ]

[MR03]

Andreas Mild and Thomas Reutterer. An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing and Consumer Services, 10(3):123-133, 2003. [ bib | find paper in Google Scholar | find paper in Google ]

[LSY03]

Greg Linden, Brent Smith, and Jeremy York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76-80, 2003. [ bib | find paper in Google Scholar | find paper in Google ]

[GSH03]

Andreas Geyer-Schulz and Michael Hahsler. Comparing two recommender algorithms with the help of recommendations by peers. In O.R. Zaiane, J. Srivastava, M. Spiliopoulou, and B. Masand, editors, WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, Lecture Notes in Computer Science LNAI 2703, pages 137-158. Springer-Verlag, 2003. [ bib | find paper in Google Scholar | find paper in Google ]

[HKTR04]

Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5-53, January 2004. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[DK04]

Mukund Deshpande and George Karypis. Item-based top-n recommendation algorithms. ACM Transations on Information Systems, 22(1):143-177, 2004. [ bib | find paper in Google Scholar | find paper in Google ]

[Dem04]

Ayhan Demiriz. Enhancing product recommender systems on sparse binary data. Data Minining and Knowledge Discovery, 9(2):147-170, 2004. [ bib | find paper in Google Scholar | find paper in Google ]

[TGR05]

Andreas Thor, Nick Golovin, and Erhard Rahm. Adaptive website recommendations with awesome. VLDB J., 14(4):357-372, 2005. [ bib | find paper in Google Scholar | find paper in Google ]

[SPUP05]

Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. Croc: A new evaluation criterion for recommender systems. Electronic Commerce Research, 5(1):51-74, 2005. [ bib | find paper in Google Scholar | find paper in Google ]

[GM05]

Thomas George and Srujana Merugu. A scalable collaborative filtering framework based on co-clustering. In ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining, pages 625-628, Washington, DC, USA, 2005. IEEE Computer Society. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[AT05]

Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749, June 2005. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[PP05]

Manos Papagelis and Dimitris Plexousakis. Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligenc, 18(7):781-789, October 2005. [ bib | find paper in Google Scholar | find paper in Google ]

[LJLK05]

Jong-Seok Lee, Chi-Hyuck Jun, Jaewook Lee, and Sooyoung Kim. Classification-based collaborative filtering using market basket data. Expert Systems with Applications, 29(3):700-704, October 2005. [ bib | find paper in Google Scholar | find paper in Google ]

[MRK06]

Sean M. McNee, John Riedl, and Joseph A. Konstan. Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI '06: CHI '06 extended abstracts on Human factors in computing systems, pages 1097-1101, New York, NY, USA, 2006. ACM. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[LCC06]

Cane Wing-Ki Leung, Stephen Chi-Fai Chan, and Fu-Lai Chung. A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems, 10(3):357-381, 2006. [ bib | find paper in Google Scholar | find paper in Google ]

[SMB07]

J. J. Sandvig, Bamshad Mobasher, and Robin Burke. Robustness of collaborative recommendation based on association rule mining. In RecSys '07: Proceedings of the 2007 ACM conference on Recommendersystems, pages 105-112. ACM, 2007. [ bib | find paper in Google Scholar | find paper in Google ]

[BKN07]

Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl, editors. The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science. Springer, Berlin, June 2007. [ bib | find paper in Google Scholar | find paper in Google ]

[ZWSP08]

Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. Large-scale parallel collaborative filtering for the netflix prize. In AAIM '08: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management, pages 337-348, Berlin, Heidelberg, 2008. Springer-Verlag. [ bib | find paper in Google Scholar | find paper in Google ]

[PZC⁺08]

Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. One-class collaborative filtering. In IEEE International Conference on Data Mining, pages 502-511, Los Alamitos, CA, USA, 2008. IEEE Computer Society. [ bib | find paper in Google Scholar | find paper in Google ]

[KBV09]

Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42:30-37, 2009. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

[SK09]

Xiaoyuan Su and Taghi M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 2009. [ bib | find paper in Google Scholar | find paper in Google ]

[ZKL⁺10]

Tao Zhou, Zoltán Kuscsik, Jian-guo Liu, Matúš Medo, Joseph Rushton, and Yi-cheng Zhang. Solving the apparent diversity-accuracy dilemma of recommender systems. PNAS, 2010. [ bib | DOI | find paper in Google Scholar | find paper in Google ]

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