Abstract
The exponential increasing of information on the Web and information retrieval systems engendered a heightened need for content personalization. Recommender systems are widely used for this purpose. Collaborative Filtering (CF) is the most popular recommendation technique. However, CF systems are very dependent on the availability of ratings to model relationships between users and generate accurate predictions. Thus, no recommendation can be computed for newly incorporated items. This paper proposes an original way to alleviate the latency problem by harnessing behavioral leaders in the context of a behavioral network. In this network, users are linked when they have a similar navigational behavior. We present an algorithm that aims at detecting behavioral leaders based on their connectivity and their potentiality of prediction. These leaders represent the entry nodes that the recommender system targets so as to predict the preferences of their neighbors about new items. This approach is evaluated in terms of precision using a real usage dataset. The results of the experimentation show that our approach not only solves the latency problem, it also leads to a precision higher than standard CF.
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art. IEEE transactions on knowledge and data engineering 17(6), 734–749 (2005)
Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: Proceedings of the International Conference on Web Search and Web Data Mining (WSDM’08), pp. 207–218. ACM, New York (2008)
Anand, S., Mobasher, B.: Intelligent techniques for web personalization. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 1–36. Springer, Heidelberg (2005)
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. ACM Commun. 40(3), 66–72 (1997)
Barabasi, A.L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaboration. Physica A 311(3-4), 590–614 (2002)
Billsus, D., Pazzani, M.: User modeling for adaptive news access. User-Modeling and User-Adapted Interaction 10(2-3), 147–180 (2000)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Cheon, H., Lee, H.: Opinion Leader Based Filtering. In: Fox, E.A., Neuhold, E.J., Premsmit, P., Wuwongse, V. (eds.) ICADL 2005. LNCS, vol. 3815, pp. 352–359. Springer, Heidelberg (2005)
Coleman, J., Menzel, H., Katz, E.: Medical Innovations: A Diffusion Study. Bobbs-Merrill Co. (1966)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD ’01: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM, New York (2001)
Elihu, K., Lazarsfeld, P.F.: Personal Influence; the Part Played by People in the Flow of Mass Communications. Free Press, New York (1955)
Esslimani, I., Brun, A., Boyer, A.: A collaborative filtering approach combining clustering and navigational based correlations. In: Proceedings of the 5th International Conference on Web Information Systems and Technologies (WEBIST), Lisbon, Portugal (2009)
Esslimani, I., Brun, A., Boyer, A.: From social networks to behavioral networks in recommender systems. In: Proceedings of The 2009 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 143–148. IEEE Computer society, Los Alamitos (2009)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence (AAAI’99/IAAI’99), Menlo Park, CA, USA, pp. 439–446. American Association for Artificial Intelligence (1999)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Discovering leaders from community actions. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management (CIKM’08), pp. 499–508. ACM, New York (2008)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), pp. 230–237. ACM, New York (1999)
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)
Keller, E., Berry, J.: The influentials. Simon and Schuster Ed. (2003)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’03), pp. 137–146. ACM, New York (2003)
Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the 12th International Conference on Machine Learning (ICML’95), pp. 331–339 (1995)
Malcolm, G.: The Tipping Point: How Little Things Can Make a Big Difference. Little Brown, New York (2000)
Middleton, S.E., Shadbolt, N.R., Roure, D.D.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)
Newman, M.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)
Popescul, A., Ungar, L., Pennock, D.M., Lawrence, S.: Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (UAI’01), pp. 437–444. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Rashid, A., Karypis, G., Riedl, J.: Influence in ratings-based recommender systems: An algorithm-independent approach
Schein, A., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’02), pp. 253–260. ACM, New York (2002)
Sollenborn, M., Funk, P.: Category-based filtering and user stereotype cases to reduce the latency problem in recommender systems. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 395–420. Springer, Heidelberg (2002)
Valente, T.W.: Network models of the diffusion of innovations. Hampton Press (1995)
Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. Journal of Consumer Research 34(4), 441–458 (2007)
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Esslimani, I., Brun, A., Boyer, A. (2010). Detecting Leaders to Alleviate Latency in Recommender Systems. In: Buccafurri, F., Semeraro, G. (eds) E-Commerce and Web Technologies. EC-Web 2010. Lecture Notes in Business Information Processing, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15208-5_21
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DOI: https://doi.org/10.1007/978-3-642-15208-5_21
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