Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems

arXiv:2312.10967v3 Announce Type: replace
Abstract: Conversational recommender systems (CRS) utilize natural language interactions and dialogue history to infer user preferences and provide accurate recommendations. Due to the limited conversation context and background knowledge, existing CRSs rely on external sources such as knowledge graphs to enrich the context and model entities based on their inter-relations. However, these methods ignore the rich intrinsic information within entities. To address this, we introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework, which leverages both the …

abstract arxiv context conversation conversational cs.ai cs.cl cs.ir dialogue graphs history interactions knowledge knowledge graphs language language models natural natural language recommendations recommender systems representation representation learning systems type