Download PDFOpen PDF in browserImproving Conversational Recommender Systems via Knowledge-Enhanced Temporal EmbeddingEasyChair Preprint 1088510 pages•Date: September 11, 2023AbstractConversational recommender systems are becoming increasingly popular due to their potential to facilitate personalized interactions between users. However, one major challenge lies in accurately representing the semantic meaning of the conversational history to make relevant recommendations. In this paper, we propose a knowledge-enhanced model KITE to enhance conversational recommender systems. To achieve a more nuanced understanding of users' evolving interests and behaviors over time, a knowledge-enhanced temporal embedding is integrated into KITE to facilitate the encoding of temporal aspects into the representation of user dialogues. Our proposal is extensively evaluated on a real conversational dataset, and the experimental results demonstrate the effectiveness and superiority of our proposals in improving the accuracy and relevance of conversational recommender systems. Our work sheds light on the potential of leveraging advanced language models to enhance the performance of conversational recommender systems. Keyphrases: Conversational Recommender Systems, Pre-trained Language Models, Temporal Embedding.
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