COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
arXiv:2008.09237
Abstract
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE. The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation. Specifically, we first construct a unified knowledge graph and extract key entities between user--product pairs, which serve as the skeleton of a conversation. Then we simulate conversations mirroring the human coarse-to-fine process of choosing preferred items. The proposed baselines and experiments demonstrate that our dataset is able to provide innovative opportunities for conversational recommendation.
References in corpus (6)
- Distributed Representations of Words and Phrases and their Compositionality
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- KGAT: Knowledge Graph Attention Network for Recommendation
- Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
- Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
- Towards Deep Conversational Recommendations