Constraint-based recommender systems: technologies and research issues

ABSTRACT

Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.

REFERENCES

• 1: Silvana Aciar , Debbie Zhang , Simeon Simoff , John Debenham, Informed Recommender: Basing Recommendations on Consumer Product Reviews, IEEE Intelligent Systems, v.22 n.3, p.39-47, May 2007 [doi>10.1109/MIS.2007.55]
• 2: 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]
• 3: N. J. Belkin , D. Kelly , G. Kim , J.-Y. Kim , H.-J. Lee , G. Muresan , M.-C. Tang , X.-J. Yuan , C. Cool, Query length in interactive information retrieval, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860474]
• 4: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Proceedings of the seventh international conference on World Wide Web 7, p.107-117, April 1998, Brisbane, Australia
• 5: Robin D. Burke , Kristian J. Hammond , Benjamin C. Young, The FindMe Approach to Assisted Browsing, IEEE Expert: Intelligent Systems and Their Applications, v.12 n.4, p.32-40, July 1997 [doi>10.1109/64.608186]
• 6: Burke, R. 2000. Knowledge-based recommender systems. Encyclopedia of Library & InformationSystems, 69(32).
• 7: Robin Burke, Hybrid Recommender Systems: Survey and Experiments, User Modeling and User-Adapted Interaction, v.12 n.4, p.331-370, November 2002 [doi>10.1023/A:1021240730564]
• 8: Chen, L., and Pu, P. 2006. Evaluating Critiquing-based Recommender Agents. 21^st National Conference on Artificial Intelligence (AAAI'06), pp. 157--162, Boston, USA.
• 9: Coester, R., Gustavsson, A., Olsson, R., and Rudstroem, A. 2002. Enhancing web-based configuration with recommendations and cluster-based help', in AH'02 Workshop on Recommendation and Personalization in E-Commerce, Malaga, Spain.
• 10: Felfernig, A., Friedrich, G., Isak, K., Shchekotykhin, K., Teppan, E, and Jannach, D. 2008. Automated Debugging of Recommender User Interface Descriptions, to appear: Journal of Applied Intelligence.
• 11: Alexander Felfernig , Erich Teppan , Gerhard Friedrich , Klaus Isak, Intelligent debugging and repair of utility constraint sets in knowledge-based recommender applications, Proceedings of the 13th international conference on Intelligent user interfaces, January 13-16, 2008, Gran Canaria, Spain [doi>10.1145/1378773.1378802]
• 12: Felfernig, A. 2007. Standardized Configuration Knowledge Representations as Technological Foundation for Mass Customization, IEEE Transactions on Engineering Management, 54(1):41--56.
• 13: Alexander Felfernig, Reducing development and maintenance efforts for web-based recommender applications, International Journal of Web Engineering and Technology, v.3 n.3, p.329-351, January 2007 [doi>10.1504/IJWET.2007.012060]
• 14: Alexander Felfernig , Gerhard Friedrich , Lars Schmidt-Thieme, Guest Editors' Introduction: Recommender Systems, IEEE Intelligent Systems, v.22 n.3, p.18-21, May 2007 [doi>10.1109/MIS.2007.52]
• 15: Felfernig, A., Isak, K., Szabo, K., and Zachar, P. 2007. The VITA Financial Services Sales Support Environment, AAAI/IAAI 2007, pp. 1692--1699, Vancouver, Canada.
• 16: Felfernig, A., Friedrich, G., Gula, B., Hitz, M., Kruggel, T., Melcher, R., Riepan, D., Strauss, S., Teppan, E., and Vitouch, O. 2007. Persuasive Recommendation: Exploring Serial Position Effects in Knowledge-based Recommender Systems, Persuasive 2007, LNCS, 4744.
• 17: 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]
• 18: Zhenhui Jiang , Weiquan Wang , Izak Benbasat, Multimedia-based interactive advising technology for online consumer decision support, Communications of the ACM, v.48 n.9, p.92-98, September 2005 [doi>10.1145/1081992.1081995]
• 19: Junker, U. 2004. QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems. 19^th National Conference on AI (AAAI'04), pp. 167--172.
• 20: Jerome Kelleher , Derek Bridge, An Accurate and Scalable Collaborative Recommender, Artificial Intelligence Review, v.21 n.3-4, p.193-213, June 2004 [doi>10.1023/B:AIRE.0000036255.53433.26]
• 21: Joseph A. Konstan , Bradley N. Miller , David Maltz , Jonathan L. Herlocker , Lee R. Gordon , John Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, v.40 n.3, p.77-87, March 1997 [doi>10.1145/245108.245126]
• 22: Feng-Yang Kuo , Tsai-Hsin Chu , Meng-Hsiang Hsu , Hong-Ssu Hsieh, An investigation of effort-accuracy trade-off and the impact of self-efficacy on Web searching behaviors, Decision Support Systems, v.37 n.3, p.331-342, June 2004 [doi>10.1016/S0167-9236(03)00032-0]
• 23: Martin, B., Sherrard, M., Wentzel, D. 2005.The Role of Sensation Seeking and Need for Cognition on Web-Site Evaluations: A Resource-Matching Perspective, Psychology and Marketing, 22(2): 109--126, Wiley.
• 24: McSherry, D. 2004. Maximally Successful Relaxations of Unsuccessful Queries. 15th Conference on Artificial Intelligence and Cognitive Science, Galway, Ireland, pp. 127--136.
• 25: Nader Mirzadeh , Francesco Ricci , Mukesh Bansal, Feature Selection Methods for Conversational Recommender Systems, Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service, p.772-777, March 29-April 01, 2005 [doi>10.1109/EEE.2005.75]
• 26: Satoshi Niwa , Takuo Doi , Shinichi Honiden, Web Page Recommender System based on Folksonomy Mining for ITNG '06 Submissions, Proceedings of the Third International Conference on Information Technology: New Generations, p.388-393, April 10-12, 2006 [doi>10.1109/ITNG.2006.140]
• 27: Payne, J., Bettman, J., and Johnson, E. 1993. The Adaptive Decision Maker. Cambridge University Press.
• 28: 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]
• 29: Pine, B., and Davis, S. 1999. Mass Customization: The New Frontier in Business Competition. Harvard Business School Press, 1999.
• 30: R Reiter, A theory of diagnosis from first principles, Artificial Intelligence, v.32 n.1, p.57-95, April 1987 [doi>10.1016/0004-3702(87)90062-2]
• 31: Paul Resnick , Hal R. Varian, Recommender systems, Communications of the ACM, v.40 n.3, p.56-58, March 1997 [doi>10.1145/245108.245121]
• 32: 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]
• 33: Francesco Ricci , Hannes Werthner, Introduction to the Special Issue: Recommender Systems, International Journal of Electronic Commerce, v.11 n.2, p.5-9, Number 2 / Winter 2006-7 [doi>10.2753/JEC1086-4415110200]
• 34: Francesco Ricci , Quang Nhat Nguyen, Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System, IEEE Intelligent Systems, v.22 n.3, p.22-29, May 2007 [doi>10.1109/MIS.2007.43]
• 35: 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]
• 36: J. Ben Schafer , Joseph A. Konstan , John Riedl, E-Commerce Recommendation Applications, Data Mining and Knowledge Discovery, v.5 n.1-2, p.115-153, January-April 2001 [doi>10.1023/A:1009804230409]
• 37: Simon, H. 1955. A Behavioral Model of Choice. Quarterly Journal of Economics, 69(1): 99--118.
• 38: Barry Smyth , Lorraine McGinty , James Reilly , Kevin McCarthy, Compound Critiques for Conversational Recommender Systems, Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, p.145-151, September 20-24, 2004 [doi>10.1109/WI.2004.45]
• 39: Thompson, C., Göker, M., and Langley, P. 2004. A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21:393--428.
• 40: Tsang, E. 1993. Foundations of Constraint Satisfaction, Academic Press, London and San Diego.
• 41: Winterfeldt, D., and Edwards, W. Decision Analysis and Behavioral Research, Cambridge University Press, Cambridge, England, 1986.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.5 Programming Languages and Software

Keywords:
constraint-based recommendation, recommender systems