FineRec:Exploring Fine-grained Sequential Recommendation

• URL: http://arxiv.org/abs/2404.12975v1
• Date: Fri, 19 Apr 2024 16:04:26 GMT
• Title: FineRec:Exploring Fine-grained Sequential Recommendation
• Authors: Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong, Hongfei Lin, Fenglong Ma,
• Abstract summary: We propose a novel framework that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. We present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations.
• Score: 28.27273649170967
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. To tackle the diversity of opinions, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several realworld datasets demonstrate the superiority of our FineRec over existing state-of-the-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.

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