Abstract
Diversity is believed to be an essential factor in improving user satisfaction in recommender systems, while how to take advantage of it has long been a problem worth exploring. Existing work either ignores the influence of diversity or overlooks users’ different diversity demands in recommendations. In this study, we analyze users’ behaviors on a real-world dataset collected from an e-commerce website and find that the demand for diversity changes among different users, even the same user’s demand varies among different shopping scenarios. There is also evidence that users’ behaviors are affected by the diversity of impressions, which has been often ignored by traditional session-based recommendation models. Then, we propose a Dynamic Diversification Recommendation Model (DDRM) with the integration of both click and impression diversities to better make use of diversity for recommendations. Extensive experimental results demonstrate that DDRM outperforms all baseline methods significantly. Further studies show our model can provide more precise and reasonable recommendations.
This work is supported by the National Key Research and Development Program of China (2018YFC0831900), Natural Science Foundation of China (Grant No. 62002191, 61672311, 61532011) and Tsinghua University Guoqiang Research Institute. This project is also funded by China Postdoctoral Science Foundation (2020M670339) and Dr. Weizhi Ma has been supported by Shuimu Tsinghua Scholar Program.
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Hao, B., Zhang, M., Guo, C., Ma, W., Liu, Y., Ma, S. (2021). Diversify or Not: Dynamic Diversification for Personalized Recommendation. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_37
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DOI: https://doi.org/10.1007/978-3-030-75765-6_37
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