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A Review-based Feature-level Information Aggregation Model for Graph Collaborative Filtering
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AI
DOI：10.1016/j.neucom.2023.126697.png)
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摘要
En 中文
To explicitly exploit the collaborative signals in the user-item interaction graph, a growing number of recent Collaborative Filtering (CF) studies adopt Graph Convolution Network (GCN) as a basis. Though effective, these methods basically treat all neighbors equally, ignoring the fact that neighbors should be target-related. While there are some works that assign different weights to neighbors via using the attention mechanism, they still suffer from two problems. First, the performance of them is limited by the lack of fine-grained details. Second, attention learned solely from interaction data may not reflect the user's opinions accurately. To address these issues, we propose a Review-based Feature-level Information Aggregation (RFIA) graph model that incorporates the review information into the graph propagation to achieve more fine-grained information aggregation. The main idea of RFIA is to assign feature-level attention vectors for the interaction edges to adaptively adjust the contribution ratios of input neighbors across various dimensions, based on rich review information. Specifically, we first extract review features from text by BERT-Whitening. Then, we design non-linear feature extractors separately in two directions to further extract and refine these review features as feature-level attention. Finally, we design a graph contrastive learning module to optimize the learning of extractors under limited user behaviors. Experiments on three publicly available datasets validate the effectiveness and performance superiority of our proposed model.
Keyword:
Graph Convolution Network
Collaborative Filtering
Contrastive learning
Recommender system
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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暂无机构信息
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