Abstract
In this paper, we propose a dual-process cognitive recommendation system for sequential recommendations. The framework includes an intuitive representation module (System 1) and an inference module (System 2). System 1 is designed to understand the user’s historical interaction sequences with external knowledge graph. System 2 is built to make recommendations by reinforcement learning to consider long-term returns and diversity. We demonstrate the performance of our method on a wide range of recommendation datasets. Experiments show significant improvement over the state-of-the-art models regarding both relevance and diversity.
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Gao, Y. et al. (2022). Efficient Dual-Process Cognitive Recommender Balancing Accuracy and Diversity. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_33
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DOI: https://doi.org/10.1007/978-3-031-00129-1_33
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