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World Wide Web
Erschienen in: World Wide Web 2/2022

16.06.2021

Reinforced KGs reasoning for explainable sequential recommendation

verfasst von: Zhihong Cui, Hongxu Chen, Lizhen Cui, Shijun Liu, Xueyan Liu, Guandong Xu, Hongzhi Yin

Erschienen in: World Wide Web | Ausgabe 2/2022

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Abstract

We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.
Vorheriger Artikel Powerful graph of graphs neural network for structured entity analysis
Nächster Artikel The art of characterization in large networks: Finding the critical attributes

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Metadaten
Titel
Reinforced KGs reasoning for explainable sequential recommendation
verfasst von
Zhihong Cui
Hongxu Chen
Lizhen Cui
Shijun Liu
Xueyan Liu
Guandong Xu
Hongzhi Yin
Publikationsdatum
16.06.2021
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 2/2022
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-021-00902-6

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