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- W4284692242 abstract "In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the development of a dialogue. In other words, potential sequential dependencies exist in conversations. However, most of the existing CRSs neglect these potential sequential dependencies. In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. We represent conversations by items and entities, and construct user sequences to discover user preferences by considering both mentioned items and entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines." @default.
- W4284692242 created "2022-07-08" @default.
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- W4284692242 date "2022-07-06" @default.
- W4284692242 modified "2023-10-16" @default.
- W4284692242 title "Improving Conversational Recommender Systems via Transformer-based Sequential Modelling" @default.
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- W4284692242 doi "https://doi.org/10.1145/3477495.3531852" @default.
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