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Cross-Grained Neural Collaborative Filtering for Recommendation
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DOI：10.1109/ACCESS.2024.3384376.png)
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摘要
En 中文
Collaborative Filtering has achieved great success in capturing users' preferences over items. However, existing techniques only consider limited collaborative signals, leading to unsatisfactory results when the user-item interactions are sparse. In this paper, we propose a Cross-grained Neural Collaborative Filtering model (CNCF), which enables recommendation more accurate and explainable. Specifically, we first construct four kinds of interaction graphs to model both fine-grained collaborative signals and coarse-grained collaborative signals, which can better compensate for the high sparsity of user-item interactions. Then we propose a fine-grained collaborative representation learning and design Light Attribute Prediction Networks ( $LAPN$ ) to capture the high-order attribute interactions and enhance the prediction accuracy. Finally, we propose a coarse-grained collaborative representation learning to represent user preferences based on diverse latent intent factors. The experiments demonstrate the high effectiveness of our proposed model.
Keyword:
Collaboration
Representation learning
Collaborative filtering
Predictive models
Older adults
Matrix converters
Vectors
Recommender systems
Graph neural networks
collaborative representation learning
graph neural networks
recommender system
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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