Knowledge Graph-enhanced Sampling for Conversational Recommender System

TL;DR

This paper introduces KGenSam, a knowledge graph-based sampling method that enhances conversational recommender systems by integrating user interaction data and external knowledge to better address exploration and exploitation challenges.

Contribution

It proposes a novel knowledge graph-enhanced sampling approach that improves user preference estimation and model updating in CRS.

Findings

KGenSam outperforms state-of-the-art methods on real-world datasets.

Significant improvements in recommendation accuracy and user preference modeling.

Effective handling of exploration and exploitation in CRS.

Abstract

The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimation of user preferences based on sparse and noisy historical data. Conversational Recommendation System (CRS) uses the interactive form of the dialogue systems to solve the intrinsic problems of traditional recommendation systems. However, due to the lack of contextual information modeling, the existing CRS models are unable to deal with the exploitation and exploration (E&E) problem well, resulting in the heavy burden on users. To address the aforementioned issue, this work proposes a contextual information enhancement model tailored for CRS, called Knowledge Graph-enhanced Sampling (KGenSam). KGenSam integrates the dynamic graph of user interaction data with the external knowledge into one heterogeneous Knowledge Graph (KG) as the contextual information environment. Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem. Experimental results on two real-world datasets demonstrate the superiority of KGenSam with significant improvements over state-of-the-art methods.