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Bi-knowledge views recommendation based on user-oriented contrastive learning

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Abstract

In recommender system, knowledge graph (KG) is usually leveraged as side information to enhance representation ability, and has been proven to mitigate the cold-start and data sparsity issues. However, due to the complexity of KG construction, it inevitably brings a large amount of noise, thus simply introducing KG into recommender system may hurt the performance of models. In addition, the current KG-based recommendation models mainly include the following issues: (1) The rich facts and semantic knowledge contained in KG are not fully explored. (2) The useless noise in KG is not effectively filtered, and the representation obtained by neighborhood aggregation shows poor quality. (3) Nodes with long-tail distribution are easily ignored and the models fail to balance the attention between popular and unpopular items. Therefore, we propose a Bi-Knowledge Views Recommendation Based on User-Oriented Contrastive Learning architecture (BUCL) to improve the representation quality and alleviate the long-tail distribution of entities. In particular, different graph embedding methods are applied to fully extract the rich facts and semantic knowledge in the KG to obtain multiple views of nodes. Based on the different representation views, a user-oriented item quality estimation method is proposed to guide the model to generate multiple augmented subgraphs. Each node provides enough negative samples to ensure that the model discriminates the same node from other nodes in differentiated subgraphs with contrastive learning. Experiments on three benchmark datasets show that BUCL consistently outperforms state-of-the-art models, alleviating the long-tail distribution problem and reducing the impact of noise.

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Data Availability

The data is available at https://github.com/RUCAIBox/RecSysDatasets.

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Acknowledgements

We thank the instructor Li Bohan for our guidance and the funds for our support.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62172351, the “14th Five-Year Plan” Civil Aerospace Pre-Research Project of China under Grant D020101, the Fund of Prospective Layout of Scientific Research for NUAA(Nanjing University of Aeronautics and Astronautics, and the Practice Innovation Program of NUAA (Grant No. xcxjh20221605).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Yi Liu, Hongrui Xuan & Bohan Li

  2. Ministry Key Laboratory for Safety-Critical Software Development and Verification, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  3. National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology, Xi’an, China

    Bohan Li

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  1. Yi Liu
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  2. Hongrui Xuan
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Contributions

Yi Liu gave his contribution to the construction of the overall model, the design and exploration of related experiments, and completed the writing of the manuscript. Hongrui Xuan participated in model design, exploration of experimental results, and review of the first manuscript. Bohan Li provided the idea through constructive discussions and gave the formulation of the overall research goals.

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Correspondence to Bohan Li.

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Liu, Y., Xuan, H. & Li, B. Bi-knowledge views recommendation based on user-oriented contrastive learning. J Intell Inf Syst 61, 611–630 (2023). https://doi.org/10.1007/s10844-023-00778-0

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