Cross-grained Neural Collaborative Filtering for Recommendation

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: Li, Chuntai, Kou, Yue, Shen, Derong, Nie, Tiezheng, Li, Dong
• Format: Article
• Language: English
• Subjects: Collaboration; Collaborative filtering; Collaborative Representation Learning; Filtration; Graph neural networks; Learning; Matrix converters; Older adults; Predictive models; Recommender System; Recommender systems; Representation learning; Representations; Vectors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3384376

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.