Abstract
State-of-the-art approaches to collaborative filtering are based on the use of an input matrix that represents each user profile as a vector in a space of items and, analogically, each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples, one has to propose a bi-relational data representation that is more flexible than the ordinary user-item ratings matrix. We propose to use a matrix, in which columns represent RDF-like triples and rows represent users, items, and relations. We show that the proposed behavioral data representation based on the use of an element-fact matrix, combined with reflective matrix processing, enables outperforming state-of-the- art collaborative filtering methods based on the use of a ’standard’ user-item matrix.
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References
Ciesielczyk, M., Szwabe, A.: RSVD-based Dimensionality Reduction for Recommender Systems. International Journal of Machine Learning and Computing 1(2), 170–175 (2011)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of Recommender Algorithms on Top-n Recommendation Tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys 2010), New York, NY, USA, pp. 39–46 (2010)
Damljanovic, D., Petrak, J., Lupu, M., Cunningham, H., Carlsson, M., Engstrom, G., Andersson, B.: Random Indexing for Finding Similar Nodes within Large RDF Graphs. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 156–171. Springer, Heidelberg (2012)
Fensel, D., van Harmelen, F.: Unifying reasoning and search to web scale. IEEE Internet Computing 11(2), 94–95 (2007)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Trans. Information Systems 22(1), 5–53 (2004)
Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-Class Collaborative Filtering. Technical Report. HPL-2008-48R1, HP Laboratories (2008)
Sindhwani, V., Bucak, S.S., Hu, J., Mojsilovic, A.: A Family of Non-negative Matrix Factorizations for One-Class Collaborative Filtering Problems. In: Proceedings of the ACM Recommender Systems Conference, RecSys 2009, New York (2009)
Singh, A.P., Gordon, G.J.: Relational Learning via Collective Matrix Factorization. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658 (2008)
Sutskever, I., Salakhutdinov, R., Tenenbaum, J.B.: Modelling Relational Data Using Bayesian Clustered Tensor Factorization. Advances in Neural Information Processing Systems 22 (2009)
Szwabe, A., Ciesielczyk, M., Janasiewicz, T.: Semantically enhanced collaborative filtering based on RSVD. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part II. LNCS, vol. 6923, pp. 10–19. Springer, Heidelberg (2011)
Szwabe, A., Misiorek, P., Walkowiak, P.: Reflective Relational Learning for Ontology Alignment. In: Omatu, S., Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 519–526. Springer, Heidelberg (2012)
Szwabe, A., Ciesielczyk, M., Misiorek, P.: Long-Tail Recommendation Based on Reflective Indexing. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS, vol. 7106, pp. 142–151. Springer, Heidelberg (2011)
van Rijsbergen, C.J.: The geometry of IR, The Geometry of Information Retrieval, pp. 73–101. Cambridge University Press, New York (2004)
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Szwabe, A., Misiorek, P., Ciesielczyk, M. (2013). Representation of Propositional Data for Collaborative Filtering. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_47
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DOI: https://doi.org/10.1007/978-3-319-00551-5_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
Online ISBN: 978-3-319-00551-5
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