Abstract
Online recommender systems continuously learn from user interactions that occur in a streaming manner. A fundamental challenge of online recommendation is to select important instances (i.e., user interactions) for model updates to achieve higher prediction accuracy while omitting noisy instances. In this paper, we study (1) how to select the best instances and (2) how to effectively utilize the selected instances in dynamic recommender environments. We present two instance selection strategies based on Self-Paced Learning and rating profiles. We integrate them with Factorization Machines to perform online updates. Moreover, we study the impact of contextual information in online updating. We conducted experiments on a real-world check-in dataset, which contains temporal contextual features. Empirical results demonstrate that ox ur instance selection strategies effectively balance the trade-off between prediction accuracy and efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Ghossein, M., Abdessalem, T., Barré, A.: Dynamic local models for online recommendation. In: Companion Proceedings of the the Web Conference 2018, pp. 1419–1423 (2018)
Baltrunas, L., Church, K., Karatzoglou, A., Oliver, N.: Frappe: understanding the usage and perception of mobile app recommendations in-the-wild. CoRR abs/1505.03014 (2015). http://arxiv.org/abs/1505.03014
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009)
Chen, J., Li, H., Xie, Q., Li, L., Liu, Y.: Streaming recommendation algorithm with user interest drift analysis. In: Shao, J., Yiu, M.L., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds.) APWeb-WAIM 2019. LNCS, vol. 11642, pp. 121–136. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26075-0_10
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090 (2011)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
He, X., Chen, T., Kan, M.Y., Chen, X.: TriRank: review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661–1670. ACM (2015)
He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549–558 (2016)
Jiang, L., Meng, D., Mitamura, T., Hauptmann, A.G.: Easy samples first: self-paced reranking for zero-example multimedia search. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 547–556 (2014)
Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Advances in Neural Information Processing Systems, pp. 1189–1197 (2010)
Matuszyk, P., Spiliopoulou, M.: Selective forgetting for incremental matrix factorization in recommender systems. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 204–215. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11812-3_18
Matuszyk, P., Vinagre, J., Spiliopoulou, M., Jorge, A.M., Gama, J.: Forgetting methods for incremental matrix factorization in recommender systems. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 947–953 (2015)
Matuszyk, P., Vinagre, J., Spiliopoulou, M., Jorge, A.M., Gama, J.: Forgetting techniques for stream-based matrix factorization in recommender systems. Knowl. Inf. Syst. 55(2), 275–304 (2018)
Nasraoui, O., Cerwinske, J., Rojas, C., Gonzalez, F.: Performance of recommendation systems in dynamic streaming environments. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 569–574. SIAM (2007)
Papagelis, M., Rousidis, I., Plexousakis, D., Theoharopoulos, E.: Incremental collaborative filtering for highly-scalable recommendation algorithms. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 553–561. Springer, Heidelberg (2005). https://doi.org/10.1007/11425274_57
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000. IEEE (2010)
Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 251–258 (2008)
Vinagre, J., Jorge, A.M.: Forgetting mechanisms for scalable collaborative filtering. J. Braz. Comput. Soc. 18(4), 271–282 (2012)
Vinagre, J., Jorge, A.M., Gama, J.: Fast incremental matrix factorization for recommendation with positive-only feedback. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 459–470. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08786-3_41
Zhang, Y., Wang, H., Lian, D., Tsang, I.W., Yin, H., Yang, G.: Discrete ranking-based matrix factorization with self-paced learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2758–2767 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Thanthriwatta, T., Rosenblum, D.S. (2021). Instance Selection for Online Updating in Dynamic Recommender Environments. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-75765-6_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75764-9
Online ISBN: 978-3-030-75765-6
eBook Packages: Computer ScienceComputer Science (R0)