Abstract
Regarding the huge amount of products, sites, information, etc., finding the appropriate need of a user is a very important task. Recommendation Systems (RS) guide users in a personalized way to objects of interest within a large space of possible options. This paper presents an algorithm for recommending movies. We break the recommendation task into two steps: (1) Grouping Like-Minded users, and (2) create model for each group to predict user-movie ratings. In the first step we use the Principal Component Analysis to retrieve latent groups of similar users. In the second step, we employ three different regression algorithms to build models and predict ratings. We evaluate our results against the SVD++ algorithm and validate the results by employing the MAE and RMSE measures. The obtained results show that the algorithm presented gives an improvement in the MAE and the RMSE of about 0.42 and 0.5201 respectively.
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References
Blei, D.M., Andrew, Y., Ng., Jordan, M.I., Lafferty, J.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 2003 (2003)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithm for collaborative filtering. In: Proceedings of the 14th Conference on UAI, pp. 43–52 (1998)
Burke, R.: The Adaptive Web, pp. 377–408. Springer, Heidelberg (2007)
Fouss, F., Pirotte, A., Renders, J.M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)
Golbeck, J., Hendler, J.: Filmtrust: movie recommendations using trust in web-based social networks. In: CCNC 2006. 3rd IEEE, vol. 1, pp. 282–286
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retrieval 4(2), 133–151 (2001)
Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: Gumo -the general user model ontology. In: User Modeling (2005)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 89–115 (2004)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989). July
Jaffali, S., Ameur, H., Jamoussi, S., Ben Hamadou, A.: Glio: a new method for grouping like-minded users. In: Transactions on Computational Collective Intelligence XVIII. LNCS, vol. 9240, pp. 44–66. Springer, Heidelberg (2015)
Jaffali, S., Jamoussi, S.: Principal component analysis neural network for textual document categorization and dimension reduction. In: 6th International Conference on SETIT, pp. 835–839 (2012)
Khabbaz, M., Lakshmanan, L.V.S.: Toprecs: top-k algorithms for item-based collaborative filtering. In: Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT ’11, pp. 213–224. ACM (2011)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD, pp. 426–434. ACM (2008)
Koren, Y.: The bellkor solution to the netflix grand prize. Netflix prize documentation (2009)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 77–118. Springer, US (2015)
Kumar, R., Verma, B.K., Rastogi, S.S.: Social popularity based SVD++ recommender system. Int. J. Comput. Appl. 33–37 (2014)
Lu, Z., Shen, H.: A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm. In: Proceedings of the 19th International Database Engineering and Applications Symposium, Japan, pp. 72–80 (2015)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings KDD Cup Workshop at SIGKDD’07, pp. 39–42 (2007)
Quinlan, J.R.: Learning with continuous classes. In: Proceedings of the Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific (1992)
Raîche, G., Walls, T.A., Magis, D., Riopel, M., Blais, J.: Non-graphical solutions for cattells scree test. Methodol.: Eur. J. Res. Methods Behav. Soc. Sci. 9(1), 23–29 (2013)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, ICML ’07, pp. 791–798, New York, NY, USA. ACM (2007)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender system—a case study. In: ACM WebKDD Workshop (2000)
Sch\(\ddot{o}\)lkopf, B., Smola, Williamson, A.J., R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)
Vapnik, V.N.: Statistical Learning Theory. Wiley (1998)
Yang, X., Liu, Y., Guo, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. (2013)
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Jaffali, S., Jamoussi, S., Hamadou, A.B., Smaili, K. (2016). Grouping Like-Minded Users for Ratings’ Prediction. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_1
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