DHGECON: A multi-round conversational recommendation method based on dynamic heterogeneous encoding

doi:10.1016/j.knosys.2023.110607

2023-08-01

PRE

Hong-Yu Yao

DOI：10.1016/j.knosys.2023.110607

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Multi-round conversational recommendation (MCR), fulfilling a real-time recommendation task for users through interactively asking attributes and recommending items, can be regarded as a multi-step Markov decision-making process. Thus, in MCR, the key point is how to appropriately guide and characterize the dynamic interactive process in the conversation and to mine the dynamic relationship among the user, items and attributes to determine such policies as when to ask (attributes) and recommend (items), what (attributes) to ask and what (items) to recommend. Recent works mainly use statistical information involved in the conversation process to characterize the conversation without comprehensively considering the dynamic relationship among the user, items and attributes and may consequently have a negative impact on accurate capture of the user's real-time preferences. To address this issue, we propose a multi-round conversational recommendation method based on dynamic heterogeneous encoding called DHGECON. Firstly, a dynamic heterogeneous graph with three types of node (users, items, and attributes) is constructed to characterize the proceeding conversation. Secondly, a heterogeneous graph-based encoder which adaptively updates the attention weight of nodes is designed to mine and represent the dynamic high-order semantic relationship among the user, items and attributes. Finally, the encoded information is fed into the decision-making module to get the action (i.e., recommending items or asking attributes) for guiding the next round conversation. Experimental results show that, compared with the state-of-the-art existing methods, the proposed method has a significant improvement in terms of major evaluation metrics over four real-world datasets.(c) 2023 Elsevier B.V. All rights reserved.

Keyword:

Multi-round conversational recommender system

Dynamic heterogeneous graph

Self-attention mechanism