Abstract
Recommender system is an effective way to solve the problem of information overload, and remarkable progress has been achieved along with the research and applications in both academic and industrial communities. However, the scalability of the conventional recommendation algorithms has been challenged by the exponential growth of the resource data size, and the increasing time span of the data also raises new requirements on the time-awareness of the algorithm. Therefore, a dynamic recommendation model monitoring the user interest drift has become an important task for streaming recommender system. In this paper, an incremental matrix factorization model named streamGBMF is proposed which utilizes the genre information as the resource feature. The proposed model can be updated in real-time according to the streaming data. To achieve the online updating, two kinds of forgetting mechanism are embedded to analyze the users’ current interest and preference accurately and timely. To evaluate the performance of our proposed model, the experiments are designed on the popular dataset MovieLens, and different algorithms are compared in streaming environment. The results show that our approach can effectively accelerate the model training process, and the recommendation performance can be improved by real-time user interest drift detection with proposed forgetting mechanisms.
This work is partially supported by National Natural Science Foundation of China (Grant No. 61602353), Natural Science Foundation of Hubei Province (Grant No. 2017CFB505) and the Fundamental Research Funds for the Central Universities (WUT:2019III054GX).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Campos, P.G., Diez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User-Adap. Inter. 24(1), 67–119 (2014)
Chandramouli, B., Levandoski, J.J., Eldawy, A., Mokbel, M.F.: StreamRec: a real-time recommender system. In: The ACM International Conference on Management of Data, pp. 1243–1246 (2011)
Chang, S., et al.: Streaming recommender systems. In: The Web Conference, pp. 381–389 (2017)
Diazaviles, E., Drumond, L., Schmidtthieme, L., Nejdl, W.: Real-time top-n recommendation in social streams. In: The ACM Conference on Recommender Systems, pp. 59–66 (2012)
Ding, Y., Li, X.: Time weight collaborative filtering. In: The 14th ACM International Conference on Information and Knowledge Management, pp. 485–492 (2005)
Guan, X., Li, C.T., Guan, Y.: Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems. IEEE Access 5, 27668–27678 (2017)
Huang, X., Wu, L., Chen, E., Zhu, H., Liu, Q., Wang, Y.: Incremental matrix factorization: a linear feature transformation perspective. In: The International Joint Conference on Artificial Intelligence, pp. 1901–1908 (2017)
Huang, Y., Cui, B., Zhang, W., Jiang, J., Xu, Y.: TencentRec: real-time stream recommendation in practice. In: The ACM International Conference on Management of Data, pp. 227–238 (2015)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 447–456 (2009)
Lin, C., Wang, L., Tsai, K.: Hybrid real-time matrix factorization for implicit feedback recommendation systems. IEEE Access 6, 21369–21380 (2018)
Ling, G., Yang, H., King, I., Lyu, M.R.: Online learning for collaborative filtering. In: The International Symposium on Neural Networks, pp. 1–8 (2012)
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)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: The Neural Information Processing Systems Conference, pp. 1257–1264 (2007)
Pan, R., et al.: One-class collaborative filtering. In: The IEEE International Conference on Data Mining, pp. 502–511 (2008)
Rendle, S., Schmidtthieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: The ACM Conference on Recommender Systems, pp. 251–258 (2008)
Salakhutdinov, R., Mnih, A., Hinton, G.E.: Restricted Boltzmann machines for collaborative filtering. In: The International Conference on Machine Learning, pp. 791–798 (2007)
Subbian, K., Aggarwal, C.C., Hegde, K.: Recommendations for streaming data. In: The 25th ACM International Conference on Information and Knowledge Management, pp. 2185–2190 (2016)
Sun, B., Dong, L.: Dynamic model adaptive to user interest drift based on cluster and nearest neighbors. IEEE Access 5, 1682–1691 (2017)
Wang, Q., Yin, H., Hu, Z., Lian, D., Wang, H., Huang, Z.: Neural memory streaming recommender networks with adversarial training. In: The 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2467–2475 (2018)
Wang, W., Yin, H., Huang, Z., Wang, Q., Du, X., Nguyen, Q.V.H.: Streaming ranking based recommender systems. In: The International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 525–534 (2018)
Xie, Q., Xiong, F., Han, T., Liu, Y., Li, L., Bao, Z.: Interactive resource recommendation algorithm based on tag information. World Wide Web - Internet Web Inf. Syst. 21(6), 1655–1673 (2018)
Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. ACM Trans. Inf. Syst. 33(3), 10:1–10:44 (2015)
Yin, H., Cui, B., Zhou, X., Wang, W., Huang, Z., Sadiq, S.W.: Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans. Inf. Syst. 35(2), 11:1–11:44 (2016)
Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng. 28(10), 2566–2581 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, J., Li, H., Xie, Q., Li, L., Liu, Y. (2019). Streaming Recommendation Algorithm with User Interest Drift Analysis. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-26075-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26074-3
Online ISBN: 978-3-030-26075-0
eBook Packages: Computer ScienceComputer Science (R0)