Please use this identifier to cite or link to this item: https://doi.org/10.1145/3394486.3403258
Title: Interactive Path Reasoning on Graph for Conversational Recommendation
Authors: Wenqiang Lei 
Gangyi Zhang
Xiangnan He 
Yisong Miao
Xiang Wang 
Liang Chen
Tat-Seng Chua 
Keywords: Conversational Recommendation
Interactive Recommendation
Recommender System
Dialogue System
Issue Date: 23-Aug-2020
Publisher: Association for Computing Machinery
Citation: Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua (2020-08-23). Interactive Path Reasoning on Graph for Conversational Recommendation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 2073 - 2083. ScholarBank@NUS Repository. https://doi.org/10.1145/3394486.3403258
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 to prune off many irrelevant candidate attributes, leading to a better chance of hitting user-preferred attributes. To demonstrate how CPR works, we propose a simple yet effective instantiation named SCPR (Simple CPR). We perform empirical studies on the multi-round conversational recommendation scenario, the most realistic CRS setting so far that considers multiple rounds of asking attributes and recommending items. Through extensive experiments on two datasets Yelp and LastFM, we validate the effectiveness of our SCPR, which significantly outperforms the state-of-the-art CRS methods EAR and CRM. In particular, we find that the more attributes there are, the more advantages our method can achieve. © 2020 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: https://scholarbank.nus.edu.sg/handle/10635/178640
ISBN: 9781450000000
DOI: 10.1145/3394486.3403258
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