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Knowledge-enhanced multi-task recommendation in hyperbolic space

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Abstract

Multi-task learning has recently inspired a series of fruitful research in the field of recommendation due to its ability to handle complex scenarios by associating information between tasks. However, various information suitable for multi-task recommender systems usually produces different degrees of noise. For example, parameter information suitable for one task will affect other tasks, and the real data that is difficult to observe correctly in Euclidean space may be regarded as noise data. To tackle this problem, we propose a novel knowledge-enhanced multi-task recommendation algorithm in hyperbolic space named KMRH. The algorithm employs the alternate training method to alleviate the parameter noise problem in complex recommendation scenarios. Specifically, we design a novel knowledge enhancement strategy in the Poincaré sphere, which exploits hyperbolic embeddings to capture knowledge graphs of complex structured data. Finally, we adopt spatial distance as a metric to distinguish positive and negative samples at different locations, thereby limiting the detrimental impact of noise components on the recommendation model. Extensive experiments on three benchmark datasets demonstrate that our proposed algorithm achieves significant improvements over other state-of-the-art algorithms.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. https://grouplens.org/datasets/movielens/1m/.

  2. http://www2.informatik.uni-freiburg.de/cziegler/BX/.

  3. https://grouplens.org/datasets/hetrec-2011/.

  4. https://github.com//haomiaocqut/ReSys_KMRH.

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Acknowledgements

The work is supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (No.KJZD-K202101105, KJQN202001136), Humanities and Social Sciences Research Program of Chongqing Municipal Education Commission (No.22SKGH302), the National Natural Science Foundation of China (No.61702063).

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Authors and Affiliations

  1. School of Artiffcial Intelligence, Chongqing University of Technology, Chongqing, 400054, China

    Junlin Zhu, Yihao Zhang, Yulin Wang, Weiwen Liao & Ruizhen Chen

  2. Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China

    Meng Yuan

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  1. Junlin Zhu
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  2. Yihao Zhang
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  3. Yulin Wang
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  4. Weiwen Liao
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Contributions

Junlin Zhu: Conceptualization, Methodology, Software, Validation, Data Curation, Writing - Original Draft, Writing - Review and Editing. Yihao Zhang: Methodology, Formal analysis, Validation, Data Curation, Writing - Review and Editing. Yulin Wang, Weiwen Liao, Ruizhen Chen and Meng Yuan : Formal analysis, Validation, Writing- Review and Editing.

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Correspondence to Yihao Zhang.

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Zhu, J., Zhang, Y., Wang, Y. et al. Knowledge-enhanced multi-task recommendation in hyperbolic space. Appl Intell 53, 28694–28710 (2023). https://doi.org/10.1007/s10489-023-05045-x

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