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LECF: recommendation via learnable edge collaborative filtering

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Abstract

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering (CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering (LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges (i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant No. 2018YFB1004403), National Natural Science Foundation of China (Grant Nos. U1936104, 61902037, 61832001), ARC Discovery Project (Grant No. DP190101985), CAAI-Huawei MindSpore Open Fund, Beijing Academy of Artificial Intelligence (BAAI), PKU-Baidu Fund (Grant No. 2019BD006), and Fundamental Research Funds for the Central Universities (Grant No. 2020RC25).

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Authors and Affiliations

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shitao Xiao, Yingxia Shao & Yawen Li

  2. Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shitao Xiao & Yingxia Shao

  3. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld, 4072, Australia

    Hongzhi Yin

  4. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanyan Shen

  5. Department of Computer Science and Technology, Peking University, Beijing, 100871, China

    Bin Cui

  6. National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, 100871, China

    Bin Cui

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  1. Shitao Xiao
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  2. Yingxia Shao
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  3. Yawen Li
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  4. Hongzhi Yin
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  5. Yanyan Shen
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  6. Bin Cui
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Correspondence to Yingxia Shao.

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Xiao, S., Shao, Y., Li, Y. et al. LECF: recommendation via learnable edge collaborative filtering. Sci. China Inf. Sci. 65, 112101 (2022). https://doi.org/10.1007/s11432-020-3274-6

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  • DOI: https://doi.org/10.1007/s11432-020-3274-6

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