Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation

• URL: http://arxiv.org/abs/2403.08246v1
• Date: Wed, 13 Mar 2024 05:00:42 GMT
• Title: Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
• Authors: Yuting Liu, Yizhou Dang, Yuliang Liang, Qiang Liu, Guibing Guo, Jianzhe Zhao, Xingwei Wang
• Abstract summary: We propose a novel textbfLight textbfSigned textbfGraph Convolution Network specifically for textbfRecommendation (textbfLSGRec) For the negative preferences within high-order heterogeneous interactions, first-order negative preferences are captured by the negative links. recommendation results are generated based on positive preferences and optimized with negative ones.
• Score: 13.300975621769396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i.e., links in a graph) with items. To accommodate the different semantics of negative and positive links, existing works utilize two independent encoders to model users' positive and negative preferences, respectively. However, these approaches cannot learn the negative preferences from high-order heterogeneous interactions between users and items formed by multiple links with different signs, resulting in inaccurate and incomplete negative user preferences. To cope with these intractable issues, we propose a novel \textbf{L}ight \textbf{S}igned \textbf{G}raph Convolution Network specifically for \textbf{Rec}ommendation (\textbf{LSGRec}), which adopts a unified modeling approach to simultaneously model high-order users' positive and negative preferences on a signed user-item interaction graph. Specifically, for the negative preferences within high-order heterogeneous interactions, first-order negative preferences are captured by the negative links, while high-order negative preferences are propagated along positive edges. Then, recommendation results are generated based on positive preferences and optimized with negative ones. Finally, we train representations of users and items through different auxiliary tasks. Extensive experiments on three real-world datasets demonstrate that our method outperforms existing baselines regarding performance and computational efficiency. Our code is available at \url{https://anonymous.4open.science/r/LSGRec-BB95}.

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