Abstract
Since user preference is drifting over time, modeling temporary dynamic recommender system has been proven to be valuable for accurate recommendation performance. However, user feedback is continuously updating while the traditional recommender system is trained off-line in batch mode so that it cant capture user taste change in time. In this paper, we build a dynamic real-time recommendation model based on implicit user feedback stream to improve both the recommendation accuracy and training efficiency. Moreover, our model has obvious advantages over the traditional approaches in diversity, interpretability, and strong robustness to hostile attack. Finally, we conduct experiments on two real world datasets to validate the effectiveness of our proposed method and demonstrate the superior performance when compared with state-of-the-art approach.
This work is supported by the National Natural Science Foundation of China (61033010, 61272065), Natural Science Foundation of Guangdong Province (S2011020001182, S2012010009311), Research Foundation of Science and Technology Plan Project in Guangdong Province ( 2011B040200007, 2012A010701013).
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References
Abernethy, J., Canini, K., Langford, J., Simma, A.: Online collaborative filtering. Tech. rep., UC Berkeley (2011)
Amatriain, X.: Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter 14(2), 37–48 (2013)
Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 661–670. ACM (2012)
Chua, F.C.T., Oentaryo, R.J., Lim, E.P.: Modeling temporal adoptions using dynamic matrix factorization. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 91–100. IEEE (2013)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)
Diaz-Aviles, E., Drumond, L., Schmidt-Thieme, L., Nejdl, W.: Real-time top-n recommendation in social streams. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 59–66. ACM (2012)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. Communications of the ACM 53(4), 89–97 (2010)
Kurucz, M., Benczur, A.A., Kiss, T., Nagy, I., Szabó, A., Torma, B.: Who rated what: a combination of svd, correlation and frequent sequence mining. In: Proc. KDD Cup and Workshop, vol. 23, pp. 720–727. Citeseer (2007)
Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 502–511. IEEE (2008)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Zhuang, Y., Chin, W.S., Juan, Y.C., Lin, C.J.: A fast parallel sgd for matrix factorization in shared memory systems. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 249–256. ACM (2013)
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Wang, Z., Li, Q., Liu, Y., Liu, W., Yin, J. (2015). Online Personalized Recommendation Based on Streaming Implicit User Feedback. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_59
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DOI: https://doi.org/10.1007/978-3-319-25255-1_59
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