Abstract
Matrix factorization technique has been successfully used in recommender systems. Currently, many variations are developed using this technique, e.g., biased matrix factorization, non-negative matrix factorization, multi-relational matrix factorization, etc. In the context of multi-relational data, this paper proposes another multi-relational approach for recommender systems by including all of the information from latent factor matrices to the prediction functions so that the models have more data to learn. To validate the proposed approach, experiments are conducted on standard datasets in recommender systems. Experimental results show that the proposed approach is promising.
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References
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, US (2011)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4:1–4:19 (2009)
Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), Washington, USA, pp. 43–52. IEEE CS (2007)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. J. Comput. 42(8), 30–37 (2009). IEEE Computer Society Press
Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M., Kriegel, H.P.: Relation prediction in multi-relational domains using matrix factorization. In: Proceedings of the NIPS 2008 Workshop: Structured Input-Structured Output, Vancouver, Canada, December, 2008
Thai-Nghe, N., Schmidt-Thieme, L.: Multi-relational factorization models for student modeling in intelligent tutoring systems. In: 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), pp. 61–66. IEEE (2015)
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 (KDD 2008). KDD 2008, pp. 650–658. ACM, New York (2008)
Drumond, L., Diaz-Aviles, E., Schmidt-Thieme, L., Nejdl, W.: Optimizing multi-relational factorization models for multipletarget relations. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014) (2014)
Thai-Nghe, N.: Predicting Student Performance in an Intelligent Tutoring System. Ph.D. thesis. University of Hildesheim, Germany (2012). hildok.bsz-bw.de
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Thai-Nghe, N., Nhut-Tu, M., Nguyen, HH. (2017). An Approach for Multi-Relational Data Context in Recommender Systems. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_66
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DOI: https://doi.org/10.1007/978-3-319-54472-4_66
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