Abstract
Collaborative filtering (CF) has been widely employed within recommender systems in many real-world situations. The basic assumption of CF is that items liked by the same user would be similar and users like the same items would share a similar interest. But it is not always true since the user’s interest changes over time. It should be more reasonable to assume that if these items are liked by the same user in the same time period, there is a strong possibility that they are similar, but the possibility will shrink if the user likes them in a different time period. In this paper, we propose a long-short interest model (LSIM) based on the new assumption to improve collaborative filtering. In special, we introduce a neural network based language model to extract the sequential features on user’s preference over time. Then, we integrate the sequential features to solve the rating prediction task in a feature based collaborative filtering framework. Experimental results on three MovieLens datasets demonstrate that our approach can achieve the state-of-the-art performance.
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Notes
- 1.
we use symbol x and y instead of classic u and v to avoid confusion between v and vector symbol \(\mathbf {v}\) in neural network language model.
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Acknowledgement
This work was supported by National High Technology R&D Program of China (Grant Nos. 2015AA015403, 2014AA015102), Natural Science Foundation of China (Grant Nos. 61202233, 61272344, 61370055) and the joint project with IBM Research. Any correspondence please refer to Yansong Feng.
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Lv, C., Yao, L., Feng, Y., Zhao, D. (2016). Improving Collaborative Filtering with Long-Short Interest Model. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_28
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DOI: https://doi.org/10.1007/978-3-319-50496-4_28
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