Abstract
Our methods so far ignore time which is an important variable that can be monitored in almost any application. In this chapter, we extend item recommendation (see chapter 6) with time information. In general, time is a continuous variable with infinite support. Thus, our factorization models in chapter 5 cannot be applied directly as they assume a categorical domain. Also simple discretization of the domain would not work because (1) factorization models assume no a priori relationship between two variable instances (e.g. two close points in time) and (2) the model could not predict in the future as no observations for these variables are present. Thus, our approach is different: we reformulate the problem with sequences and use the independence assumptions of Markov chains within our model. That means for each user, we see his action of the past as a sequence – e.g. what products he has bought. Typically, several products are bought at the same day and thus, we have per user a sequences of sets (=baskets/ shopping carts). The Markov chain assumption is now that the next action (=shopping cart) of the user depends only on a few of his previous ones.
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References
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: IEEE International Conference on Data Mining (ICDM 2008), pp. 263–272 (2008)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456. ACM, New York (2009)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Using sequential and non-sequential patterns in predictive web usage mining tasks. In: ICDM 2002: Proceedings of the 2002 IEEE International Conference on Data Mining, p. 669. IEEE Computer Society, Washington (2002)
Pan, R., Scholz, M.: Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 667–676. ACM, New York (2009)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) (2009)
Shani, G., Heckerman, D., Brafman, R.I.: An mdp-based recommender system. Journal of Machine Learning Research 6, 1265–1295 (2005)
Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. In: UAI 2001: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, pp. 580–588. Morgan Kaufmann Publishers Inc., San Francisco (2001)
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Rendle, S. (2010). Sequential-Set Recommendation. In: Context-Aware Ranking with Factorization Models. Studies in Computational Intelligence, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16898-7_8
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DOI: https://doi.org/10.1007/978-3-642-16898-7_8
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