FlexRecs: expressing and combining flexible recommendations

ABSTRACT

Recommendation systems have become very popular but most recommendation methods are `hard-wired' into the system making experimentation with and implementation of new recommendation paradigms cumbersome. In this paper, we propose FlexRecs, a framework that decouples the definition of a recommendation process from its execution and supports flexible recommendations over structured data. In FlexRecs, a recommendation approach can be defined declaratively as a high-level parameterized workflow comprising traditional relational operators and new operators that generate or combine recommendations. We describe a prototype flexible recommendation engine that realizes the proposed framework and we present example workflows and experimental results that show its potential for capturing multiple, existing or novel, recommendations easily and having a flexible recommendation system that combines extensibility with reasonable performance.

REFERENCES

• 1: Problems with recommendations: url: http://www.amazon.com/problemswith-kindle-store-recommendations/forum/fxbvkst06pwp9b/tx1togp7gq4xa4t/1?_encoding=utf8&asin=b000fi73ma.
• 2: The Stanford Daily: url: http://stanforddaily.com/article/2007/12/5/-editorialcourserankalongoverduesuccess.
• 3: Gediminas Adomavicius , YoungOk Kwon, New Recommendation Techniques for Multicriteria Rating Systems, IEEE Intelligent Systems, v.22 n.3, p.48-55, May 2007 [doi>10.1109/MIS.2007.58]
• 4: Gediminas Adomavicius , Alexander Tuzhilin, Multidimensional Recommender Systems: A Data Warehousing Approach, Proceedings of the Second International Workshop on Electronic Commerce, p.180-192, November 16-17, 2001
• 5: Gediminas Adomavicius , 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, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 6: Marko Balabanović , Yoav Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [doi>10.1145/245108.245124]
• 7: Daniel Billsus , Michael J. Pazzani, User Modeling for Adaptive News Access, User Modeling and User-Adapted Interaction, v.10 n.2-3, p.147-180, 2000 [doi>10.1023/A:1026501525781]
• 8: J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In 14th UAI Conf., 1998.
• 9: Latha S. Colby, A recursive algebra for nested relations, Information Systems, v.15 n.5, p.567-582, 1990 [doi>10.1016/0306-4379(90)90029-O]
• 10: Abhinandan S. Das , Mayur Datar , Ashutosh Garg , Shyam Rajaram, Google news personalization: scalable online collaborative filtering, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242610]
• 11: V. Deshpande and P. Larson. Transforming from flat algebra to nested algebra. System Sciences, 2(2-5):298--307, 1990.
• 12: David Goldberg , David Nichols , Brian M. Oki , Douglas Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, v.35 n.12, p.61-70, Dec. 1992 [doi>10.1145/138859.138867]
• 13: Jonathan L. Herlocker , Joseph A. Konstan , Loren G. Terveen , John T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.5-53, January 2004 [doi>10.1145/963770.963772]
• 14: George Karypis, Evaluation of Item-Based Top-N Recommendation Algorithms, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502627]
• 15: Georgia Koutrika , Robert Ikeda , Benjamin Bercovitz , Hector Garcia-Molina, Flexible recommendations over rich data, Proceedings of the 2008 ACM conference on Recommender systems, October 23-25, 2008, Lausanne, Switzerland [doi>10.1145/1454008.1454041]
• 16: Georgia Koutrika , Benjamin Bercovitz , Robert Ikeda , Filip Kaliszan , Henry Liou , Hector Garcia-Molina, Flexible Recommendations for Course Planning, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.1467-1470, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.127]
• 17: Greg Linden , Brent Smith , Jeremy York, Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing, v.7 n.1, p.76-80, January 2003 [doi>10.1109/MIC.2003.1167344]
• 18: Michael O'Mahony , Neil Hurley , Nicholas Kushmerick , Guénolé Silvestre, Collaborative recommendation: A robustness analysis, ACM Transactions on Internet Technology (TOIT), v.4 n.4, p.344-377, November 2004 [doi>10.1145/1031114.1031116]
• 19: Bradley N. Miller , Istvan Albert , Shyong K. Lam , Joseph A. Konstan , John Riedl, MovieLens unplugged: experiences with an occasionally connected recommender system, Proceedings of the 8th international conference on Intelligent user interfaces, January 12-15, 2003, Miami, Florida, USA [doi>10.1145/604045.604094]
• 20: Bamshad Mobasher , Robin Burke , Runa Bhaumik , Chad Williams, Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness, ACM Transactions on Internet Technology (TOIT), v.7 n.4, p.23-es, October 2007 [doi>10.1145/1278366.1278372]
• 21: Michael Pazzani , Daniel Billsus, Learning and Revising User Profiles: The Identification ofInteresting Web Sites, Machine Learning, v.27 n.3, p.313-331, June 1997 [doi>10.1023/A:1007369909943]
• 22: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 23: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]
• 24: Andrew I. Schein , Alexandrin Popescul , Lyle H. Ungar , David M. Pennock, Methods and metrics for cold-start recommendations, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564421]
• 25: H J Schek , M H Scholl, The relational model with relation-valued attributes, Information Systems, v.11 n.2, p.137-147, 1986 [doi>10.1016/0306-4379(86)90003-7]
• 26: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 27: B. Sheth and P. Maes. Evolving agents for personalized information filtering. In IEEE Conf. Artificial Intelligence for Applications, 1993.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Languages, Performance

Keywords:
flexible recommendations, recommendation operators, recommendation queries