MACR: Multi-information Augmented Conversational Recommender

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Conversational recommender systems (CRS) aim to provide high-quality recommendations through fewer multi-turn conversations. However, because short conversation histories lack sufficient item information, CRSs not only struggle to make accurate recommendations but also lack diversity in the generated responses. Existing CRSs mainly alleviate these problems by introducing external information (e.g., reviews) while ignoring information inside the conversations (e.g., potential category preferences in user utterances). Besides, item introduction is a kind of external information that is more objective and contains more entities than reviews. Therefore, we propose a Multi-information Augmented Conversational Recommender (MACR), which improves the performance of recommendation and response generation by mining the underlying category preferences in users’ utterances and incorporating item introductions. Specifically, we enhance the category associations among entities by constructing a knowledge graph DBMG with category nodes, extracting and encoding the item categories that match the user preferences into the user representation. For item introductions, we extract the entities in them and fuse them into the conversation using an introduction-attentive encoder–decoder. Extensive experiments on the dataset REDIAL show that our MACR significantly outperforms previous state-of-the-art approaches. The source code will be available at https://github.com/zcy-cqut/MACR.