Abstract
As e-commerce has evolved into its second generation, where the available products are becoming more complex and their abundance is almost unlimited, the task of locating a desired choice has become too difficult for the average user. Therefore, more effort has been made in recent years to develop recommender systems that recommend products or services to users so as to assist in their decision-making process. In this article, we describe crucial experimental results about a novel recommender technology, called the preference-based organization (Pref-ORG), which generates critique suggestions in addition to recommendations according to users' preferences. The critique is a form of feedback (“I would like something cheaper than this one”) that users can provide to the currently displayed product, with which the system may better predict what the user truly wants. We compare the preference-based organization technique with related approaches, including the ones that also produce critique candidates, but without the consideration of user preferences. A simulation setup is first presented, that identified Pref-ORG's significantly higher algorithm accuracy in predicting critiques and choices that users should intend to make, followed by a real-user evaluation which practically verified its significant impact on saving users' decision effort.
- Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Engin. 17, 6, 734--749. Google ScholarDigital Library
- Agrawal, R., Imielinski, T. and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD 1993, 207--216. Google ScholarDigital Library
- Bidel, S., Lemoine, L., Piat, F., Artieres, T., and Gallinari, P. 2003. Statistical machine learning for tracking hypermedia user behaviour. In Proceedings of the 2nd Workshop on Machine Learning, Information Retrieval, and User Modeling.Google Scholar
- Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, 43--52. Google ScholarDigital Library
- Burke, R. 2000. Knowledge-based recommender systems. Encyclopedia of Library and Information Systems 69.Google Scholar
- Burke, R., Hammond, K., and Cooper, E. 1996. Knowledge-based navigation of complex information spaces. In Proceedings of the 13th National Conference on Artificial Intelligence, 462--468. Google ScholarDigital Library
- Burke, R., Hammond, K., and Young, B. 1997. The FindMe approach to assisted browsing. IEEE Expert: Intel. Syst. Appl. 12, 32--40. Google ScholarDigital Library
- Carenini, G. and Moore, J. 1998. Multimedia explanations in IDEA decision support system. Working Notes of AAAI Spring Symposium on Interactive and Mixed-Initiative Decision Theoretic Systems, 16--22.Google Scholar
- Carenini, G. and Poole, D. 2000. Constructed preferences and value-focused thinking: implications for AI research on preference elicitation. In Proceedings of the AAAI-02 Workshop on Preferences in AI and CP: Symbolic Approaches.Google Scholar
- Chen, L. and Pu, P. 2005. Trust building in recommender agents. In Proceedings of the Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces at the 2nd International Conference on E-Business and Telecommunication Networks, 135--145.Google Scholar
- Chen, L. and Pu, P. 2006. Evaluating critiquing-based recommender agents. In Proceedings of the 21st National Conference on Artificial Intelligence, 157--162. Google ScholarDigital Library
- Chen, L. and Pu, P. 2007a. Hybrid critiquing-based recommender systems. In Proceedings of the International Conference on Intelligent User Interfaces, 22--31. Google ScholarDigital Library
- Chen, L. and Pu, P. 2007b. Preference-based organization interfaces: aiding user critiques in recommender systems. In Proceedings of the International Conference on User Modeling, 77--86. Google ScholarDigital Library
- Cristianini, N. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines. Cambridge University Press. Google ScholarDigital Library
- Faltings, B., Pu, P., Torrens, M., and Viappiani, P. 2004. Designing example-critiquing interaction. In Proceedings of the International Conference on Intelligent User Interfaces (IUI'04), 22--29. Google ScholarDigital Library
- Frias-Martinez, E., Chen, S. Y., and Liu, X. 2006. Survey of data mining approaches to user modelling for adaptive hypermedia. IEEE Trans. Syst. Man Cyber. 36, 6, 734--749. Google ScholarDigital Library
- Friedman, J. H., Baskett, F., and Shustek, L. J. 1975. An algorithm for finding nearest neighbors. IEEE Trans. Comput. 24, 10, 1000--1006. Google ScholarDigital Library
- Grabner-Kräuter, S. and Kaluscha, E. A. 2003. Empirical research in on-line trust: a review and critical assessment. Int. J. Human-Comput. Stud. 58, 783--812. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., and Riedl, J. 2000. Explaining collaborative filtering recommendations. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 241--250. Google ScholarDigital Library
- Keeney, R. and Raiffa, H. 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press.Google Scholar
- Klein, D. A. and Shortliffe, E. H. 1994. A framework for explaining decision-theoretic advice. Artif. Intel. 67, 201--243. Google ScholarDigital Library
- Koller, D. and Sahami, M. 1997. Hierarchically classifying documents using very few words. In Proceedings of the 14th International Conference on Machine Learning, 170--178. Google ScholarDigital Library
- Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: applying collaborative filtering to usenet news. Comm. ACM 40, 3, 77--87. Google ScholarDigital Library
- Linden, G., Hanks, S., and Lesh, N. 1997. Interactive assessment of user preference models: The automated travel assistant. In Proceedings of the International Conference on User Modeling, 67--78.Google Scholar
- McCarthy, K., Reilly, J., McGinty, L., and Smyth, B. 2004a. On the dynamic generation of compound critiques in conversational recommender systems. In Proceedings of the 3rd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 176--184.Google Scholar
- McCarthy, K., Reilly, J., McGinty, L., and Smyth, B. 2004b. Thinking positively--explanatory feedback for conversational recommender systems. In Proceedings of the Workshop on Explanation in CBR at the 7th European Conference on Case-Based Reasoning, 115--124.Google Scholar
- McCarthy, K., McGinty, L., Smyth, B., and Reilly, J. 2005a. A live-user evaluation of incremental dynamic critiquing. In Proceedings of the International Conference on Case-based Reasoning (ICCBR), 339--352. Google ScholarDigital Library
- McCarthy, K., McGinty, L., Smyth, B., and Reilly, J. 2005b. On the evaluation of dynamic critiquing: A large-scale user study. In Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, 535--540. Google ScholarDigital Library
- McCarthy, K., Reilly, J. McGinty, L., and Smyth, B. 2005c. Experiments in dynamic critiquing. In Proceedings of International Conference on Intelligent User Interfaces, 175--182. Google ScholarDigital Library
- McGinty, L. and Smyth, B. 2003. On the role of diversity in conversational recommender systems. In Proceedings of the 5th International Conference on Case-Based Reasoning, 276--290. Google ScholarDigital Library
- McKnight, D. H. and Chervany, N. L. 2002. What trust means in e-commerce customer relationships: Conceptual typology. Int. J. Electron. Commerce. 35--59. Google ScholarDigital Library
- McSherry, D. 2002. Diversity-conscious retrieval. In Proceedings of the European Conference on Case-based reasoning, 219--233. Google ScholarDigital Library
- McSherry, D. 2003. Similarity and compromise. In Proceedings of the International Conference on Case-Based Reasoning Research and Development (ICCBR'03), 291--305. Google ScholarDigital Library
- McSherry, D. 2004. Explanation in recommender systems. In Workshop Proceedings of the 7th European Conference on Case-Based Reasoning, 125--134.Google Scholar
- Miller, B., Konstan, J., Terveen, L., and Riedl, J. 2004. PocketLens: Towards a personal recommender system. ACM Trans. Inform. Syst. 22, 3, 437--476. Google ScholarDigital Library
- Mooney, R. J., Bennett, P. N., and Roy, L. 1998. Book recommending using text categorization with extracted information. In Proceedings of the AAAI Workshop on Recommender Systems, 70--74.Google Scholar
- Payne, J. W., Bettman, J. R., and Johnson, E. J. 1993. The Adaptive Decision Maker. Cambridge University Press.Google Scholar
- Payne, J. W., Bettman, J. R., and Schkade, D. A. 1999. Measuring constructed preference: towards a building code. J. Risk Uncert 19. 1--3, 243--270.Google Scholar
- Pazzani, M. and Billsus, D. 1997. Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27, 313--331. Google ScholarDigital Library
- Poole, A. and Ball, L. J. 2005. Eye tracking in human-computer interaction and usability research: current status and future prospects. In Ghaoui, C. (Ed.), Encylopedia of Human Computer Interaction, Idea Group.Google Scholar
- Price, B. and Messinger, P.R. 2005. Optimal recommendation sets: covering uncertainty over user preferences. In Proceedings of National Conference on Artificial Intelligence (AAAI'05), 541--548. Google ScholarDigital Library
- Pu, P. and Chen, L. 2005. Integrating trade-off support in product search tools for e-commerce sites. In Proceeding of the ACM Conference on Electronic Commerce, 269--278. Google ScholarDigital Library
- Pu, P. and Chen, L. 2006. Trust building with explanation interfaces. In Proceedings of International Conference on Intelligent User Interfaces, 93--100. Google ScholarDigital Library
- Pu, P. and Chen, L. 2007. Trust-inspiring explanation interfaces for recommender systems. Know.-Based Syst. J. 20, 542--556. Google ScholarDigital Library
- Pu, P. and Faltings, B. 2000. Enriching buyers' experiences: the SmartClient approach. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 289--296. Google ScholarDigital Library
- Pu, P. and Kumar, P. 2004. Evaluating example-based search tools. In Proceeding of the ACM Conference on Electronic Commerce, 208--217. Google ScholarDigital Library
- Pu, P., Kumar, P., and Faltings, B. 2003. User-involved trade-off analysis in configuration tasks. In Proceedings of the Workshop Notes, the 3rd International Workshop on User-Interaction in Constraint Satisfaction, 9th International Conference on Principles and Practice of Constraint Programming.Google Scholar
- Reilly, J., McCarthy, K., McGinty, L., and Smyth, B. 2004. Dynamic critiquing. In Proceedings of the European Conference on Case-based Reasoning (ECCBR), 763--777.Google Scholar
- Reilly, J., McCarthy, K., McGinty, L., and Smyth, B. 2005. Incremental critiquing. J. Knowl.-Based Syst. 18, 4--5, 143--151. Google ScholarDigital Library
- Reilly, J., Zhang, J., McGinty, L., Pu, P., and Smyth, B. 2007. Evaluating compound critiquing recommenders: A real-user study. In Proceedings of the ACM Conference on Electronic Commerce, 114--123. Google ScholarDigital Library
- Ricci, F. and Nguyen, Q. N. 2007. Preferences acquisition and revision in a critique-based mobile recommender system. IEEE Intel. Syst. 22, 3, 22--29. Google ScholarDigital Library
- Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., and Riedl, J. 1998. Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 345--354. Google ScholarDigital Library
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference. Google ScholarDigital Library
- Shimazu, H. 2001. ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing. In Proceedings of the 17th International Joint Conference on Artificial Intelligence. Google ScholarDigital Library
- Shimazu, H. 2002. ExpertClerk: a conversational case-based reasoning tool for developing salesclerk agents in e-commerce webshops. Artif. Intel. Rev. 18, 223--244. Google ScholarDigital Library
- Sinha, R. and Swearingen, K. 2002. The role of transparency in recommender systems. In Extended Abstracts of Conference on Human Factors in Computing Systems (CHI'02), 830--831. Google ScholarDigital Library
- Smyth, B. and McGinty, L. 2003. An analysis of feedback strategies in conversational recommenders. In Proceedings of the 14th Irish Artificial Intelligence and Cognitive Science Conference.Google Scholar
- Swartout, W., Paris, C. and Moore, J. 1991. Explanations in knowledge systems: design for explainable expert systems. IEEE Intell. Syst. Appl. 6, 3, 58--64. Google ScholarDigital Library
- Thompson, C. A., Goker, M. H., and Langley, P. 2004. A personalized system for conversational recommendations. J. Artif. Intel. Res. 21, 393--428. Google ScholarDigital Library
- Torrens, M., Faltings, B., and Pu, P. 2002. SmartClients: constraint satisfaction as a paradigm for scalable intelligent information systems. Int. J. Constraits 7, 1, 49--69. Google ScholarDigital Library
- Torrens, M., Weigel, R., and Faltings, B. 1997. Java constraint library: bringing constraints technology on the Internet using the Java language. In Proceedings of the Workshop of National Conference on Artificial Intelligence (AAAI), 10--15.Google Scholar
- Tversky, A. and Simonson, I. 1993. Context-dependent preferences. Manage. Sci. 39, 10, 1179--1189.Google ScholarDigital Library
- Viappiani, P., Faltings, B., and Pu, P. 2007. Preference-based search using example-critiquing with suggestions. J. Artif. Intel. Res. 27, 465--503. Google ScholarDigital Library
- Webb, G. I., Pazzani, M. J., and Billsus, D. 2001. Machine learning for user modelling, User Model. User-Adapt. Inter. 11, 1--2, 19--29. Google ScholarDigital Library
- Williams, M. D. and Tou, F. N. 1982. RABBIT: an interface for database access. In Proceedings of the ACM Conference, 83--87. Google ScholarDigital Library
- Xia, Z., Dong, Y., and Xing, G. 2006. Support vector machines for collaborative filtering. In Proceedings of the 44th ACM Annual Southeast Regional Conference, 169--174. Google ScholarDigital Library
- Zhang, J. and Pu, P. 2006. A comparative study of compound critique generation in conversational recommender systems. In Proceedings of International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 234--243. Google ScholarDigital Library
- Zhang, J. and Pu, P. 2007. Refining preference-based search results through bayesian filtering. In Proceedings of the International Conference on Intelligent User Interfaces, 294--297. Google ScholarDigital Library
- Zhu, T., Greiner, R., and Haubl, G. 2003. Learning a model of a Web user's interests. In Proceedings of 9th International Conference on User Modeling, 148--157. Google ScholarDigital Library
- Ziegler, C. N., McNee, S. M., Konstan, J. A., and Lausen, G. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International World Wide Web Conference, 22--32. Google ScholarDigital Library
Index Terms
- Experiments on the preference-based organization interface in recommender systems
Recommendations
Using Groups of Items for Preference Elicitation in Recommender Systems
CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social ComputingTo achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by ...
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Effect of the Number of Users and Bias of Users’ Preference on Recommender Systems
Intelligent Data Engineering and Automated Learning - IDEAL 2007AbstractRecommender System provides certain products adapted to a target user, from a large number of products. One of the most successful recommendation algorithms is Collaborative Filtering, and it is used in many websites. However, the recommendation ...
Comments