MCKP: Multi-aspect contextual knowledge-enhanced prompting for conversational recommender systems

Abstract

Empowering conversational recommender systems (CRSs) with knowledge facilitates the generation of high-quality human-like recommendation proposals to users. Despite substantial endeavors in developing knowledge-based CRSs, they mainly focus on modeling entity knowledge by introducing external data, which cannot sufficiently contribute to understanding complex contextual semantics. Additionally, the source of knowledge relies on external data, ignoring the effective use of original training data, significantly increasing the cost of building efficient CRSs.To tackle the issues above, we explore how to leverage raw dialogue data for modeling and exploiting contextual knowledge to compensate for existing knowledge's shortcomings. Specifically, we first train knowledge generators by constructing multiple standard hybrid prompt templates and answer spaces, which is conducive to perceiving the reasons behind contextual connections from multiple aspects and storing corresponding contextual knowledge. Then, we design answer-guided prompt templates based on the answer space, closing the gap between inference and training of knowledge generators and facilitating the generation of aspect-specific contextual knowledge. Next, we extend our approach by harnessing external knowledge graphs to construct entity knowledge. We propose a local-to-global semantic fusion strategy to fuse knowledge representations from different sources better. Finally, based on the fused knowledge representations, we tailor contextual knowledge-enhanced prompt templates for the CRS to stimulate the reasoning ability of pre-trained language models on dialogue and recommendation tasks. We evaluate the performance of our method on separate evaluation datasets, including the ReDial and the INSPIRED datasets. Based on the Recall, MRR, NDCG, and Distinct evaluation indicators, we demonstrate the effectiveness of the proposed method in dialogue tasks and recommendation tasks. At the same time, we demonstrate that by constructing an interpretable multi-aspect hybrid prompt template, we can effectively mine contextual knowledge from multiple aspects of dialogue.