Article

Multidimensional Recommender Systems: A Data Warehousing Approach

Authors:

Gediminas Adomavicius,

Alexander TuzhilinAuthors Info & Claims

WELCOM '01: Proceedings of the Second International Workshop on Electronic Commerce

Pages 180 - 192

Published: 16 November 2001 Publication History

Abstract

In this paper, we present a new data-warehousing-based approach to recommender systems. In particular, we propose to extend traditional two-dimensional user/item recommender systems to support multiple dimensions, as well as comprehensive profiling and hierarchical aggregation (OLAP) capabilities. We also introduce a new recommendation query language RQL that can express complex recommendations taking into account the proposed extensions. We describe how these extensions are integrated into a framework that facilitates more flexible and comprehensive user interactions with recommender systems.

References

[1]

G. Adomavicius and A. Tuzhilin. Expert-driven validation of rule-based user models in personalization applications. Journal of Data Mining and Knowledge Discovery, 5(1/2):33-58, 2001.

[2]

A. Ansari, S. Essegaier, and R. Kohli. Internet recommendations systems. Journal of Marketing Research, pages 363-375, August 2000.

[3]

M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):66-72, 1997.

[4]

P. Baudisch, editor. CHI'99 Workshop: Interacting with Recommender Systems, May 1999. http://www.darmstadt.gmd.de/rec99/.

[5]

J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. Technical Report MSR-TR-98-12, Microsoft Research, May 1998.

[6]

A. Buchner and M. Mulvenna. Discovering internet marketing intelligence through online analytical web usage mining. SIGMOD Record, 27(4):54-61, 1998.

[7]

Communications of the ACM, 40(3):56-89, 1997. Special issue on Recommender Systems.

[8]

Communications of the ACM, 43(8):26-158, 2000. Special issue on Personalization.

[9]

A. Caglayan, M. Snorrason, J. Jacoby, J. Mazzu, R. Jones, and K. Kumar. Learn sesame - a learning agent engine. Applied Artificial Intelligence, 11:393-412, 1997.

[10]

S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1):65-74, 1997.

[11]

H. Kautz, editor. Recommender Systems. Papers from 1998 Workshop. Technical Report WS-98-08. AAAI Press, 1998.

[12]

R. Kimball. The Data Warehouse Toolkit. John Wiley & Sons, Inc., 1996.

[13]

J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3):77-87, 1997.

[14]

K. Lang. Newsweeder: Learning to filter netnews. In Proceedings of the 12th International Conference on Machine Learning, 1995.

[15]

R. J. Mooney, P. N. Bennett, and L. Roy. Book recommending using text categorization with extracted information. In Recommender Systems. Papers from 1998 Workshop. Technical Report WS-98-08. AAAI Press, 1998.

[16]

Movie Critic. http://www.moviecritic.com.

[17]

M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, pages 393-408, December 1999.

[18]

M. Pazzani, J. Muramatsu, and D. Billsus. Syskill & Webert: Identifying interesting web sites. In Proceedings of the National Conference on Artificial Intelligence, 1996.

[19]

P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 Computer Supported Cooperative Work Conference, 1994.

[20]

J. B. Schafer, J. A. Konstan, and J. Riedl. E-commerce recommendation applications. Journal of Data Mining and Knowledge Discovery, 5(1/2):115-153, 2001.

[21]

U. Shardanand and P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. In Proceedings of the Conference on Human Factors in Computing Systems (CHI'95), pages 210-217. ACM Press, 1995.

[22]

J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2):12-23, 2000.

Cited By

Oliveira YSilveira LSouza CNeto MNovais RFerraz CViana W(2018)A Model-Driven Approach to Evolve Recommender SystemsProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3267457(169-172)Online publication date: 16-Oct-2018
https://dl.acm.org/doi/10.1145/3243082.3267457
Zhang ZXu GZhang P(2016)Research on E-Commerce Platform-Based Personalized Recommendation AlgorithmApplied Computational Intelligence and Soft Computing10.1155/2016/51604602016(2)Online publication date: 1-Mar-2016
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Deng TFan WGeerts F(2015)On recommendation problems beyond points of interestInformation Systems10.1016/j.is.2014.08.00248:C(64-88)Online publication date: 1-Mar-2015
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cover image Guide Proceedings

WELCOM '01: Proceedings of the Second International Workshop on Electronic Commerce

November 2001

232 pages

ISBN:354042878X

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 16 November 2001

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Cited By

Oliveira YSilveira LSouza CNeto MNovais RFerraz CViana W(2018)A Model-Driven Approach to Evolve Recommender SystemsProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3267457(169-172)Online publication date: 16-Oct-2018
https://dl.acm.org/doi/10.1145/3243082.3267457
Zhang ZXu GZhang P(2016)Research on E-Commerce Platform-Based Personalized Recommendation AlgorithmApplied Computational Intelligence and Soft Computing10.1155/2016/51604602016(2)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1155/2016/5160460
Deng TFan WGeerts F(2015)On recommendation problems beyond points of interestInformation Systems10.1016/j.is.2014.08.00248:C(64-88)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.is.2014.08.002
Domingues MJorge ASoares C(2013)Dimensions as Virtual ItemsInformation Processing and Management: an International Journal10.1016/j.ipm.2012.07.00949:3(698-720)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1016/j.ipm.2012.07.009
Deng TFan WGeerts FLenzerini MBenedikt M(2012)On the complexity of package recommendation problemsProceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems10.1145/2213556.2213592(261-272)Online publication date: 21-May-2012
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Lee SMille AGandon FMisselis JRabinovich MStaab S(2012)A generic graph-based multidimensional recommendation framework and its implementationsProceedings of the 21st International Conference on World Wide Web10.1145/2187980.2188002(161-166)Online publication date: 16-Apr-2012
https://dl.acm.org/doi/10.1145/2187980.2188002
Hoan NNguyen V(2012)Advance missing data processing for collaborative filteringProceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II10.1007/978-3-642-34707-8_36(355-364)Online publication date: 28-Nov-2012
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Kahng MLee SLee SNica ASuchanek F(2011)Ranking objects by following paths in entity-relationship graphsProceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management10.1145/2065003.2065008(11-18)Online publication date: 28-Oct-2011
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Umyarov ATuzhilin A(2011)Using external aggregate ratings for improving individual recommendationsACM Transactions on the Web10.1145/1921591.19215945:1(1-40)Online publication date: 17-Feb-2011
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Huang ZLu XDuan H(2011)Context-aware recommendation using rough set model and collaborative filteringArtificial Intelligence Review10.1007/s10462-010-9185-735:1(85-99)Online publication date: 1-Jan-2011
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