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Metapath-guided dual semantic-aware filtering for HIN-based recommendation

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Abstract

Many heterogeneous information network (HIN)-based recommendation methods leverage the semantic and structural features of metapath to improve the recommendation performance. However, the existing HIN-based recommendation methods using metapath still suffer from two challenges: (1) HINs in industrial recommendation scenarios usually have a very large scale and contain much redundant or noisy structural information, which may damage the efficiency and effectiveness of the recommendation model. (2) HINs include rich metapath semantic information that may be noisy and irrelevant to downstream tasks. To address the above two challenges, we propose a metapath-guided dual semantic-aware filtering for HIN-based recommendation from two perspectives: intra-metapath and inter-metapath (called MFGRec). Our model first develops a neighbor filtering method within metapath-guided attribute networks to generate tailored metapath-guided attribute networks for filtering irrelative or noise neighbors of intra-metapath. Moreover, our model designs a semantic-aware filtering-based fusion method using a novel adaptive multi-head sparse attention mechanism to automatically discard the irrelative metapath-guided attribute networks for each user-item interaction pair and assign personalized weight to the selected valuable networks for distinguishing the semantic differences of inter-metapath. In general, MFGRec filters a large amount of noise and irrelevant information from intra-metapath and inter-metapath perspectives, which significantly improves the scalability and accuracy of the recommendation framework. Furthermore, experimental results on three publicly accessible datasets and nine baselines demonstrate that our model achieves higher accuracy of recommendation and lower runtime costs compared with existing state-of-the-art methods.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the https://github.com/librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding.

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Funding

This work was supported by the Natural Science Foundation of China [grant numbers 61972337, 61502414].

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Authors and Affiliations

  1. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

    Surong Yan, Haosen Wang, Yixiao Li, Chunqi Wu, Chenglong Shi & Ruilin Guo

  2. School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Long Han

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  1. Surong Yan
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  2. Haosen Wang
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  3. Yixiao Li
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Contributions

Surong Yan and Haosen Wang wrote the main manuscript text. Yixiao Li and Ruilin Guo reviewed the manuscript. Haosen Wang, Chenglong Shi, Long Han and Chunqi Wu conducted software.

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Correspondence to Haosen Wang.

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Yan, S., Wang, H., Li, Y. et al. Metapath-guided dual semantic-aware filtering for HIN-based recommendation. J Supercomput 79, 11934–11964 (2023). https://doi.org/10.1007/s11227-023-05113-6

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