Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation. However, most CRS approaches do not effectively utilize the signal provided by these conversations. They rely heavily on explicit external knowledge e.g., knowledge graphs to augment the models' understanding of the items and attributes, which is quite hard to scale. To alleviate this, we propose an alternative information retrieval (IR)-styled approach to the CRS item recommendation task, where we represent conversations as queries and items as documents to be retrieved. We expand the document representation used for retrieval with conversations from the training set. With a simple BM25-based retriever, we show that our task formulation compares favorably with much more complex baselines using complex external knowledge on a popular CRS benchmark. We demonstrate further improvements using user-centric modeling and data augmentation to counter the cold start problem for CRSs.

翻译：对话式推荐系统（CRS）旨在通过自然语言对话向用户推荐合适的物品。然而，大多数CRS方法未能有效利用对话提供的信号。它们严重依赖显式外部知识（例如知识图谱）来增强模型对物品及其属性的理解，这很难扩展。为缓解这一问题，我们提出了一种基于信息检索（IR）范式的替代方法来解决CRS物品推荐任务，其中我们将对话表示为查询，将物品表示为待检索的文档。我们利用训练集中的对话扩展了用于检索的文档表示。通过一个简单的基于BM25的检索器，我们证明了我们的任务形式化在一个流行的CRS基准测试中，与使用复杂外部知识的、复杂得多的基线方法相比具有优势。我们还展示了通过以用户为中心的建模和数据增强来应对CRS冷启动问题的进一步改进。