Abstract
Nowadays, internet has offer an overabundance of available information. In social networks, users confront gigantic number of items. To overcome this phenomenon, known as information overload, recommender systems are intended to filter information and help users to make their choice. Many models based collaborative filtering have been used in the literature to solve the problem of recommendation. Among these models, latent factor model has become the most popular due to his performed results of accuracy. This work is part of research into Recommender System domain and aims to present a detailed explication on works based latent factor model. We first describe a general view of this model. Its realization in field of recommendation is next presented. A detailed study on different steps is then exposed. The most important works that have been developed are then presented. To the author’s knowledge, there has been no work that tries to explain in detail how latent factor model is applied to Recommender Systems.
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References
Jallouli, M., Lajmi, S., Amous, I.: Similarity and trust metrics used in recommender systems: a survey. In: Madureira, A.M., Abraham, A., Gamboa, D., Novais, P. (eds.) ISDA 2016. AISC, vol. 557, pp. 1041–1050. Springer, Cham (2017). doi:10.1007/978-3-319-53480-0_102
Yehuda, K., Robert, B., Volinsky, C.: Matrix factorisation techniques for recommender systems. IEEE Computer Society (2009)
Anind, K.D., Gregory, D.: Towards a better understanding of context and context-awareness. In: Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing, Karlsruhe, Germany (1999)
Weijia, S.: Tensor Completion. Saarland University (2012)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems (NIPS) (2008)
Delporte, J., Karatzoglou, A., Matuszczyk, T., Canu, S.: Socially enabled preference learning from implicit feedback data. In: Proceedings, Part II, of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pp. 145–160 (2013)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: proceeding of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrival (SIGIR), pp. 203–210 (2009)
Jamal, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social. In: Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys), pp. 135–142 (2010)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 123–129 (2015)
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. 21(4), 1253–1278 (2000)
Harshman, R.A.: Foundations of the PARAFAC Procedure: Models and Conditions for an “explanatory” Multi-modal Factor Analysis, vol. 1, p. 16. University of california, Los Angelos (1970)
Datta, B.: Numerical Linear Algebra and Application. Brooks/Cole Publishing Company, Pacific Grove (1995)
Karatzoglou, A., Amatriain, X., Baltrunas, L.: Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In: Recommender Systems 2010, Barcelona, Spain (2010)
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., Oliver, N.: TFMAP: optimizing MAP for Top-N context-aware recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 155–164 (2012)
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Jallouli, M., Lajmi, S., Amous, I. (2017). Latent Factor Model Applied to Recommender System: Realization, Steps and Algorithm. In: Themistocleous, M., Morabito, V. (eds) Information Systems. EMCIS 2017. Lecture Notes in Business Information Processing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-65930-5_47
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DOI: https://doi.org/10.1007/978-3-319-65930-5_47
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