Interactive Path Reasoning on Graph for Conversational Recommendation

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 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 [13] and CRM [24]. In particular, we find that the more attributes there are, the more advantages our method can achieve. CCS CONCEPTS • Information systems → Users and interactive retrieval; Recommender systems; Personalization; • Human-centered computing → Interactive systems and tools.

A review of recommendation system research based on bipartite graph

“…Another point that can be studied in the future is to use graph structure for path reasoning. The KARN model proposed by Cao et al [18] and the SCPR model proposed by Lei et al [19] both perform recommendations by reasoning about the path between users and items. From recent research results, it can be seen that the path reasoning recommendation based on graph neural network is still in its infancy, and more extensive attempts are needed in the future.…”

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

“…In addition, previous works also explore the effectiveness of structured knowledge by either encoding the nodes (Yang and Mitchell, 2017;, triples (Mihaylov and Frank, 2018;), paths (Lin et al, 2019Lei et al, 2020) or tabular (Zhu et al, 2021a) (Saha and Mausam, 2018) is widely used in knowledge base question answering to extract entity-relation triples (Bosselut et al, 2019;Zhao et al, 2020;Deng et al, 2019). However, OpenIE favors precision over recall, which is not necessarily effective to form connections among diverse evidence facts for multi-hop QA.…”

An Implicit Preference‐Aware Sequential Recommendation Method Based on Knowledge Graph

“…The relationship can be effectively inferred on the path to infer the basic principles of the interaction between the user and the item. Lei et al [28] proposed a conversational path reasoning (CPR) model, which can model conversational recommendation as a graphical interactive path reasoning problem. Through user feedback, this method traverses the attribute vertices in an explicit way.…”