Learning What to Ask: Mining Product Attributes for E-commerce Sales from Massive Dialogue Corpora

Yan Fan, Chengyu Wang, Fan Feng, Hengbin Cui, Yuchuan Wu, Yongbin Li

Abstract: Conversational Recommender Systems (CRSs) are extensively applied in e-commercial platforms that recommend items to users. To ensure accurate recommendation, agents usually ask for users' preferences towards specific product attributes which are pre-defined by humans. In e-commercial platforms, however, the number of products easily reaches to billions, making it prohibitive to pre-define decisive attributes for efficient recommendation due to the lack of substantial human resources and the scarce domain expertise. In this work, we present AliMeMOSAIC, a novel knowledge mining and conversational assistance framework that extracts core product attributes from massive dialogue corpora for better conversational recommendation experience. It first extracts user-agent interaction utterances from massive corpora that contain product attributes. A Joint Attribute and Value Extraction (JAVE) network is designed to extract product attributes from user-agent interaction utterances. Finally, AliMeMOSAIC generates attribute sets that frequently appear in dialogues as the target attributes for agents to request, and serve as an assistant to guide the dialogue flow. To prove the effectiveness of AliMeMOSAIC, we show that it consistently improves the overall recommendation performance of our CRS system. An industrial demonstration scenario is further presented to show how it benefits online shopping experiences.