Interactive Path Reasoning on Graph for Conversational Recommendation
Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang,, Liang Chen, Tat-Seng Chua

TL;DR
This paper introduces a graph-based conversational path reasoning framework for recommendation systems, explicitly utilizing user feedback on attributes to improve multi-round conversational recommendations.
Contribution
It proposes a novel graph-based framework, CPR, and its instantiation SCPR, for explicit attribute-based reasoning in conversational recommendation systems, outperforming existing methods.
Findings
SCPR significantly outperforms state-of-the-art CRS methods.
The effectiveness of SCPR increases with more attributes.
Empirical validation on Yelp and LastFM datasets confirms improvements.
Abstract
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference. Conversational recommendation system (CRS) brings revolutions to those limitations by enabling the system to directly ask users about their preferred attributes on items. However, existing CRS methods do not make full use of such advantage -- they only use the attribute feedback in rather implicit ways such as updating the latent user representation. In this paper, we propose Conversational Path Reasoning (CPR), a generic framework that models conversational recommendation as an interactive path reasoning problem on a graph. It walks through the attribute vertices by following user feedback, utilizing the user preferred attributes in an explicit way. By leveraging on the graph structure, CPR is able…
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