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- W4384788259 abstract "Reinforcement learning is playing an increasingly important role in the field of recommender systems. In this paper, we enhance the performance of a reinforcement learning-based recommender system by incorporating a knowledge graph, which serves as the embedding method for translating entities and relationships into vectors. To recommend diverse items, we define an explorative reward function for the Markov decision process that determines the recommendation. We also describe an action space pruning strategy that narrows down the reasoning space, and present a target for policy gradient optimization. Our experimental results show that the proposed method improves the recommendation performance and provides rational explanations." @default.
- W4384788259 created "2023-07-20" @default.
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- W4384788259 date "2023-01-01" @default.
- W4384788259 modified "2023-09-26" @default.
- W4384788259 title "Recommendations Based on Reinforcement Learning and Knowledge Graph" @default.
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- W4384788259 doi "https://doi.org/10.1007/978-3-031-36819-6_28" @default.
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