Abstract
Collaborative filtering is one of the most used techniques in recommender systems. The goal of this paper is to propose a new method that uses latent topics to model the items to be recommended. In this way, the ability to establish a similarity between these elements is incorporated, improving the performance of the recommendation made. The performance of the proposed method has been measured in two very different contexts, yielding satisfactory results. Finally, the conclusions and some future lines of work are included.
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Cheng, X., Yan, X., Lan, Y., Guo, J.: BTM: Topic modeling over short texts. IEEE Trans. Knowl. Data Eng. 26(12), 2928–2941 (2014)
Herlocker, J.L., Konstan, J.A., 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, pp. 230–237. ACM (1999)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
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, pp. 285–295. ACM (2001)
Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: 2007 Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 43–52. IEEE (2007)
Takács, G., Pilászy, I., Németh, B., Tikk, D.: Major components of the gravity recommendation system. ACM SIGKDD Expl. Newslett. 9(2), 80–83 (2007)
California State University: Merlot - multimedia educational resource for learning and online teaching (2017). https://merlot.org. Accessed 30 June 2017
GroupLens Research: Movielens datasets (2017). https://grouplens.org/datasets/movielens/. Accessed 30 June 2017
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(Suppl 1), 5228–5235 (2004)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 262–272. Association for Computational Linguistics (2011)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Gupta, V., Lehal, G.S.: A survey of common stemming techniques and existing stemmers for Indian languages. J. Emerg. Technol. Web Intell. 5(2), 157–161 (2013)
Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 471–475. SIAM (2005)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)
Koren, Y., Sill, J.: Collaborative filtering on ordinal user feedback. In: IJCAI, pp. 3022–3026 (2013)
Charnelli, M.E., Lanzarini, L., Diaz, J.: Modeling students through analysis of social networks topics. In: XXII Congreso Argentino de Ciencias de la Computacion CACIC 2016, pp. 363–371 (2016)
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Charnelli, M.E., Lanzarini, L., Díaz, J. (2018). Recommender System Based on Latent Topics. In: De Giusti, A. (eds) Computer Science – CACIC 2017. CACIC 2017. Communications in Computer and Information Science, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-319-75214-3_17
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DOI: https://doi.org/10.1007/978-3-319-75214-3_17
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