Abstract
In this paper we present a framework for learning models for Recommender Systems (RS) in the case where there are multiple implicit feedback associated to items. Based on a set of features, representing the dyads of users and items extracted from an implicit feedback collection, we propose a stochastic gradient descent algorithm that learn jointly classification, ranking and embeddings for users and items. Our experimental results on a subset of the collection used in the RecSys 2016 challenge for job recommendation show the effectiveness of our approach with respect to single task approaches and paves the way for future work in jointly learning models for multiple implicit feedback for RS.
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Notes
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We make available the extracted dataset as well as the codes for research purpose.
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References
Bennett, J., Lanning, S.: The Netflix prize. In: KDD Cup and Workshop 2007, p. 35 (2007)
Ben-David, S., Schuller, R.: Exploiting task relatedness for multiple task learning. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 567–580. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45167-9_41
Caruana, R.: Multitask Learning. Mach. Learn. 28(1), 41–75 (1997)
Yang, X., Seyoung, K., Xing, E.P.: Heterogeneous multi-task learning with joint sparsity constraints. In: Advances in Neural Information Processing Systems 22, Vancouver, pp. 2151–2159 (2009)
Sculley, D.: Combined regression and ranking. In: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, pp. 979–988 (2010)
Kumar, A., Daume, H.: Learning task grouping and overlap in multi-task learning. In: 29th International Conference on Machine Learning, New York, pp. 1383–1390 (2012)
Chapelle, O., Shivaswamy, P., Vadrevu, S., Weinberger, K., Zhang, Y., Tseng, B.: Multi-task learning for boosting with application to web search ranking. In: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, pp. 1189–1198 (2010)
Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 109–117 (2004)
Stock, M., Pahikkala, T., Airola, A., De Baets, B., Waegeman, W.: Efficient Pairwise Learning using Kernel Ridge Regression: an Exact Two-Step Method. Technical report (2016)
Volkovs, M., Zemel, R.S.: Collaborative ranking with 17 parameters. In: Advances in Neural Information Processing Systems 25, Lake Tahoe, pp. 2294–2302 (2012)
Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses. Springer Texts in Statistics. Springer, New York (2005). https://doi.org/10.1007/0-387-27605-X
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Moura, S., Asarbaev, A., Amini, MR., Maximov, Y. (2018). Heterogeneous Dyadic Multi-task Learning with Implicit Feedback. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_58
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DOI: https://doi.org/10.1007/978-3-030-04182-3_58
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