Abstract
Precise prediction of users’ next choices in time is critical for users’ satisfaction and platforms’ benefit. A user’s next choice heavily depends on the user’s long-term preference and recent actions. However, existing methods either (1) ignore the long-term personalized preference or the recent sequential actions of users, or (2) can’t update the model in time when receiving users’ new action information. To solve these problems, we propose an online personalized next-item recommendation method via long short term preference learning. The proposed method integrates the information of users’ long-term personalized preference and short-term sequential actions to predict the next choices. The trained model could be updated online via an extra preference transition matrix. Experimental results on our real-world datasets show that the proposed method consistently outperforms several state-of-the-art methods.
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Acknowledgement
This work was partially sponsored by National Key R&D Program of China (Grant No. 2017YFB10 020 02) and PKU-Tencent joint research Lab.
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Du, Y., Liu, H., Qu, Y., Wu, Z. (2018). Online Personalized Next-Item Recommendation via Long Short Term Preference Learning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_70
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DOI: https://doi.org/10.1007/978-3-319-97304-3_70
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