Abstract
The graph convolution network(GCN), as an advanced deep learning technique, has been effectively implemented in the collaborative filtering recommendation system. However, existing graph convolution-based collaborative filtering models neglect a pervasive issue, which is the distinction between users’ activity and sensitivity for items’ popularity, while the traditional neighbor aggregations in GCN have addressed this issue through Graph Laplacian Normalization, which produces suboptimal personalized outcomes. To solve this problem, this paper proposes a Degree-aware embedding and Interaction feature fusion-based Graph Convolution Collaborative Filtering (Di-GCCF). Firstly, the degree-aware embedding utilizes the degree of nodes in the user-item graph to reflect users’ activity and items’ popularity. Moreover, it combines users’ activity and popularity preferences to generate feature representations of users and items, which enables the model to capture their personalized preferences better. Secondly, the interaction feature fusion merges shallow and high-order features during propagation to improve the representation ability of high-order GCN. Finally, this paper performs comprehensive experiments on four public datasets to validate the effectiveness of our proposed model, and the results demonstrate the superior performance of our model from both analytical and empirical perspectives.
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This work was supported by the Science Fund for Outstanding Youth of Xinjiang Uygur Autonomous Region under Grant No. 2021D01-E14.
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Ma, C., Qin, J., Gao, A. (2023). Degree-Aware Embedding and Interactive Feature Fusion-Based Graph Convolution Collaborative Filtering. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14119. Springer, Cham. https://doi.org/10.1007/978-3-031-40289-0_10
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