Abstract
Personalized recommendation is attracting more and more attentions nowadays. There are many kinds of algorithms for making predictions for the target users, and among them Collaborative Filtering (CF) is widely adopted. In some domains, a user’s behavior sequences reflect his/her preferences over items so that users who have similar behavior sequences may indicate they have similar preference models. Based on this fact, we discuss how to improve the collaborative filtering algorithm by using user behavior sequence similarity. We proposed a new Behavior Sequence Similarity Measurement (BSSM) approach. Then, different ways to combine BSSM with CF algorithm are presented. Experiments on two real test data sets prove that more precise and stable recommendation performances can be achieved.
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References
AL Spector, J., Koicz, A., Karunanithi, N.: Feature-based and Clique-based User Models for Movie Selection: A Comparative Study. User Modeling and User-Adapted Interaction 7(4), 279–304 (1997), doi:10.1023/A:1008286413827
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (WWW 2001), pp. 285–295. ACM, New York (2001), doi:10.1145/371920.372071
Cao, L.: In-depth Behavior Understanding and Use: the Behavior Informatics Approach. Information Science 180(17), 3067–3085 (2010)
Lekakos, G., Giaglis, G.M.: A hybrid approach for improving predictive accuracy of collaborative filtering algorithms. User Modeling and User-Adapted Interaction 17(1-2), 5–40 (2007), doi:10.1007/s11257-006-9019-0
Good, N., Ben Schafer, J., 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 1999/IAAI 1999), pp. 439–446. American Association for Artificial Intelligence, Menlo Park (1999)
Xu, G., Zhang, Y., Yi, X.: Modelling User Behaviour for Web Recommendation Using LDA Model. In: 2008 IEEE/WIC/ACM International Confernce on Web Intelligence and Intelligent Agent Technology (2008)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann (2011) ISBN-13: 978-0123814791
Hlavacs, H., Kotsis, G.: Modeling User Behavior: A Layered Approach. In: Proceedings of the 7th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 1999), p. 218. IEEE Computer Society, Washington, DC (1999)
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), doi:10.1145/192844.192905
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3) (1997)
Joachims, T., Freitag, D., Mitchell, T.: Webwatcher: A Tour Guide for the World Wide Web. In: The 15th International Joint Conference on Artificial Intelligence (IJCAI 1997), Nagoya, Japan, pp. 770–777 (1997)
Lieberman, H.: Letizia: An Agent that Assists Web Browsing. In: Proc. of the 1995 International Joint Conference on Artificial Intelligence, pp. 924–929. Morgan Kaufmann, Montreal (1995)
Spiliopoulou, M., Faulstich, L.C.: WUM: A Web Utilization Miner. In: Proceedings of EDBT Workshop WebDB9 (1998)
Schafer, J.B., Konstan, J.A., Riedl, J.: Electronic-commerce recommender systems. J. Data Mining Knowl. Discov. 5(1), 115–152 (2001)
Su, X., Khoshgoftaar, T.M.: A Survey of Collaberative Filtering Techniques. Advances in Atificial Intelligence 2009, Article ID 421425, 19 Pages (2009), doi:10.1155/2009/421425
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Zhang, Y., Cao, J. (2013). Personalized Recommendation Based on Behavior Sequence Similarity Measures. In: Cao, L., et al. Behavior and Social Computing. BSIC BSI 2013 2013. Lecture Notes in Computer Science(), vol 8178. Springer, Cham. https://doi.org/10.1007/978-3-319-04048-6_15
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DOI: https://doi.org/10.1007/978-3-319-04048-6_15
Publisher Name: Springer, Cham
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