Abstract
The recommendation system can recommend information to users personally and efficiently, which satisfies the user’s demand for information in the information age, and has become a hot topic in the current era. In the recommendation system, users and items and the interaction of their own information has a crucial impact on the efficiency and accuracy of the recommendations. However, most of the existing recommendation systems usually design the systems as user-base only, considering the user’s influence on the item in the recommendation, which to some extent blurs the interaction between items and users at the item level, unknown and potential connections between items and users are not well considered. In this paper, we propose a collaborative memory network that can focus on the potential relation between items and users, and consider the impact of items’ characteristics on user behavior. Experiments have shown that our improvement is better than the original method and other baseline models.
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Acknowledgment
This work is supported in part by the National Natural Science Foundation of China (No. 61877043) and the National Natural Science Foundation of China (No. 61877044).
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Yu, M. et al. (2019). Paper Recommendation with Item-Level Collaborative Memory Network. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_13
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DOI: https://doi.org/10.1007/978-3-030-29551-6_13
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