Abstract
We consider applications of user preference rule learning in marketing. We chose rules because of human-understandability. We chose fuzzy logic because it enables to order items for recommendation. In this paper we introduce a rule based system equivalent to the Fagin-Lotem-Naor preference system. We show a multi-user version, introduce induction and compare it to several methods for learning user preference. The methods are based, first, on interpreting e-shop user’s behavioral patterns collected by scripts as fictitious explicit rating. After this we use this (fictitious) explicit rating for content based preference learning.
Our main motivation is on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals are not too loyal and e.g. refuse to register or provide any/enough explicit feedback. Furthermore, products such as tours, cars or furniture have very low average consumption rate preventing us from tracking unregistered user between two consecutive purchases. Recommending on such domains proves to be very challenging, yet interesting research task. As a test bed, we have conducted several off-line experiments with real user data from travel agency website confirming competitiveness of our method.
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References
Claypool, M., Le, P., Wased, M., Brown, D.: Implicit interest indicators. In: IUI 2001, pp. 33–40. ACM, New York (2001)
Eckhardt, A.: Similarity of users’ (content-based) preference models for Collaborative filtering in few ratings scenario. Expert Syst. Appl. 39(14), 11511–11516 (2012)
Eckhardt, A., Horváth, T., Maruščák, D., Novotný, R., Vojtáš, P.: Uncertainty Issues and Algorithms in Automating Process Connecting Web and User. In: da Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Nickles, M., Pool, M. (eds.) URSW 2005 - 2007. LNCS (LNAI), vol. 5327, pp. 207–223. Springer, Heidelberg (2008)
Eckhardt, A., Horváth, T., Vojtáš, P.: Learning Different User Profile Annotated Rules for Fuzzy Preference Top-k Querying. In: Prade, H., Subrahmanian, V.S. (eds.) SUM 2007. LNCS (LNAI), vol. 4772, pp. 116–130. Springer, Heidelberg (2007)
Eckhardt, A., Horváth, T., Vojtás, P.: PHASES: A User Profile Learning Approach for Web Search. Web Intelligence, 780–783 (2007)
Eckhardt, A., Pokorný, J., Vojtás, P.: A System Recommending Top-k Objects for Multiple Users Preferences. In: FUZZ-IEEE, pp. 1–6 (2007)
Eckhardt, A., Vojtáš, P.: Learning user preferences for 2cp-regression for a recommender system. In: van Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds.) SOFSEM 2010. LNCS, vol. 5901, pp. 346–357. Springer, Heidelberg (2010)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. J. Computer System Sciences 66, 614–656 (2003)
Horváth, T., Sudzina, F., Vojtás, P.: Mining Rules from Monotone Classification Measuring Impact of Information Systems on Business Competitiveness. In: Camarinha-Matos, L.M. (ed.) Emerging Solutions for Future Manufacturing Systems. IFIP, vol. 159, pp. 451–458. Springer, Boston (2004)
Horváth, T., Vojtáš, P.: Induction of Fuzzy and Annotated Logic Programs. In: Muggleton, S.H., Otero, R., Tamaddoni-Nezhad, A. (eds.) ILP 2006. LNCS (LNAI), vol. 4455, pp. 260–274. Springer, Heidelberg (2007)
Kifer, M., Subrahmanian, V.S.: Theory of Generalized Annotated Logic Programming and its Applications. J. Log. Program. 12(3&4), 335–367 (1992)
Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Springer, Netherlands (2000)
Krajci, S., Lencses, R., Vojtás, P.: A comparison of fuzzy and annotated logic programming. Fuzzy Sets and Systems 144(1), 173–192 (2004)
Peska, L., Eckhardt, A., Vojtás, P.: UPComp - A PHP Component for Recommendation Based on User Behaviour. In: Web Intelligence/IAT Workshops, pp. 306–309 (2011)
Peska, L., Vojtas, P.: Evaluating Various Implicit Factors in E-commerce. In: RUE (RecSys) 2012 ACM RecSys Workshop on Recommendation Utility Evaluation: Beyond RMSE, CEUR, vol. 910, pp. 51–55 (2012)
Peska, L., Vojtas, P.: Recommending for Disloyal Customers with Low Consumption Rate. In: Geffert, V., Preneel, B., Rovan, B., Štuller, J., Tjoa, A.M. (eds.) SOFSEM 2014. LNCS, vol. 8327, pp. 455–465. Springer, Heidelberg (2014)
Vojtás, P.: Fuzzy logic programming. Fuzzy Sets and Systems 124(3), 361–370 (2001)
Vojtáš, P., Vomlelová, M.: On models of comparison of multiple monotone classifications. In: IPMU 2006 - Information Processing and management under Uncertainty, pp. 1236–1243. Éditions EDK, Paris (2006) ISBN: 2-84254-112-X
Vojtás, P., Vomlelová, M.: Trasformation of deductive and inductive tasks between models of logic programming with imperfect information. In: Bouchon-Meunier, B., et al. (eds.) Proc. IPMU 2004, pp. 839–846. Editrice Universita La Sapienza, Roma (2004)
W3C Uncertainty Reasoning for the World Wide Web Incubator Group, http://www.w3.org/2005/Incubator/urw3/XGR-urw3/
Eckhardt, A.: Prefwork - A framework for testing of methods for user preference learning, https://code.google.com/p/prefwork/
Raś, Z.W., Wieczorkowska, A.A.: Action-rules: how to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative Filtering for Implicit Feedback Datasets. In: ICDM 2008, pp. 263–272. IEEE Computer Society, Washington, DC (2008)
Lee, D.H., Brusilovsky, P.: Reinforcing Recommendation Using Implicit Negative Feedback. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 422–427. Springer, Heidelberg (2009)
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Peska, L., Vojtas, P. (2014). Interpreting Web Shop User’s Behavioral Patterns as Fictitious Explicit Rating for Preference Learning. In: Bikakis, A., Fodor, P., Roman, D. (eds) Rules on the Web. From Theory to Applications. RuleML 2014. Lecture Notes in Computer Science, vol 8620. Springer, Cham. https://doi.org/10.1007/978-3-319-09870-8_19
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