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).
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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
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