A conversational recommender system (CRS) is a practical application for item recommendation through natural language conversation. Such a system estimates user interests for appropriate personalized recommendations. Users sometimes have various interests in different categories or genres, but existing studies assume a unique user interest that can be covered by closely related items. In this work, we propose to model such multiple user interests in CRS. We investigated its effects in experiments using the ReDial dataset and found that the proposed method can recommend a wider variety of items than that of the baseline CR-Walker.

翻译：对话推荐系统（CRS）是通过自然语言对话实现商品推荐的实际应用。此类系统通过评估用户兴趣以提供个性化推荐。用户有时会存在跨越不同类别或体裁的多样化兴趣，但现有研究通常假设用户具有单一且可由高度相关商品覆盖的兴趣。本文提出在对话推荐系统中对多用户兴趣进行建模。我们利用ReDial数据集开展实验探究该方法的效果，结果表明所提方法能比基线方法CR-Walker推荐更丰富的商品类别。