Abstract
Recommender systems (RS) are designed to assist users by recommending them items they should appreciate. User based RS exploits users behavior to generate recommendations. Users act in accordance with different modes when using RS, so RS’s performance fluctuates across users, depending on their act mode. Act here includes quantitative and qualitative features of user behavior. When RS is applied in an e-commerce dedicated social network, these features include but are not limited to: user’s number of ratings, user’s number of friends, the items he chooses to rate, the value of his ratings, and the reputation of his friends. This set of features can be considered as the user’s profile.
In this work, we cluster users according to their acting profiles, then we compare the performance of three different recommenders on each cluster, to explain RS’s performance fluctuation across different users’ acting modes.
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References
Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Burke, R., Mobasher, B., Zabicki, R., Bhaumik, R.: Identifying attack models for secure recommendation. In: Beyond Personalization: A Workshop on the Next Generation of Recommender Systems (2005)
Steck, H.: Item popularity and recommendation accuracy. In: Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys 2011), pp. 125–132. ACM, New York (2011)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Miller, B.N., Konstan, J.A., Riedl, J.: PocketLens: Toward a personal recommender system. ACM Trans. Inf. Syst. 22(3), 437–476 (2004)
Massa, P., Bhattacharjee, B.: Using Trust in Recommender Systems: An Experimental Analysis. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 221–235. Springer, Heidelberg (2004)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (CSCW 1994), pp. 175–186. ACM, New York (1994)
Golbeck, J., Hendler, J.: FilmTrust: movie recommendations using trust in web-based social networks (2006)
Maltz, D., Ehrlich, K.: Pointing the way: active collaborative filtering. In: Katz, I.R., Mack, R., Marks, L., Rosson, M.B., Nielsen, J. (eds.) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1995), pp. 202–209. ACM Press/Addison-Wesley Publishing Co., New York (1995)
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7(4), Article 23 (October 2007)
Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th International Conference on World Wide Web (WWW 2004), pp. 393–402. ACM, New York (2004)
O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4(4), 344–377 (2004)
Golbeck, J.: Personalizing applications through integration of inferred trust values in semantic web-based social networks. In: Semantic Network Analysis Workshop at the 4th International Semantic Web Conference (November 2005)
Kuter, U., Golbeck, J.: Using probabilistic confidence models for trust inference in Web-based social networks. ACM Trans. Internet Technol. 10(2), Article 8, 23 pages (2010)
Ziegler, C.-N., Lausen, G.: Spreading Activation Models for Trust Propagation. In: Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2004), pp. 83–97. IEEE Computer Society, Washington, DC (2004)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Massa, P., Avesani, P.: Trust-Aware Collaborative Filtering for Recommender Systems. In: Meersman, R. (ed.) OTM 2004, Part I. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004)
Haydar, C., Boyer, A., Roussanaly, A.: Hybridising collaborative filtering and trust-aware recommender systems. In: WEBIST 2012 - Proceedings of the 8th International Conference on Web Information Systems and Technologies, Porto, Portugal, April 18-21 (2012)
Haydar, C., Roussanaly, A., Boyer, A.: Analyzing Recommender System’s Performance Fluctuations across Users. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds.) CD-ARES 2012. LNCS, vol. 7465, pp. 390–402. Springer, Heidelberg (2012)
Alsabti, K., Ranka, S., Singh, V.: An efficient k-means clustering algorithm. Electrical Engineering and Computer Science, Paper 43 (1997)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intell. 1, 224–227 (1979)
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Haydar, C., Roussanaly, A., Boyer, A. (2012). Clustering Users to Explain Recommender Systems’ Performance Fluctuation. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_41
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DOI: https://doi.org/10.1007/978-3-642-34624-8_41
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