PLASTIC: Prioritize long and short-term information in top-n recommendation using adversarial training

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Abstract

Recommender systems provide users with ranked lists of items based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and items that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of items' attributes in short terms. In this paper, we propose a PLASTIC model, Prioritizing Long And Short-Term Information in top-n reCommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next item to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of items from the real list recorded. Extensive experiments show that our model exhibits significantly better performances on two widely used datasets.1.

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Zhao, W., Wang, B., Ye, J., Gao, Y., Yang, M., & Chen, X. (2018). PLASTIC: Prioritize long and short-term information in top-n recommendation using adversarial training. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2018-July, pp. 3676–3682). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/511

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