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Attribute-aware deep attentive recommendation

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Abstract

Since the rich semantics of attribute information has become a great supplement to the ratings data in designing recommender systems, fusing attributes information into ratings has shown promising performance for many recommendation tasks. However, the use of attribute information is not easy, because different attributes are often: (1) multi-source, that is, attributes may come from many different fields, (2) unstructured, (3) unbalanced, (4) heterogeneous. In this paper, we explore effective fusion of user-item ratings and item attributes to improve recommendations, we propose an attribute-aware deep attentive recommendation model, which embeds attribute information into the latent semantic space of items through the attention mechanism, forming more accurate item representations. Extensive experiments show that our method is superior to the existing methods on both rating prediction and Top-N Recommendation tasks.

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Authors and Affiliations

  1. Northeast Normal University School of Information Science and Technology, Changchun, China

    Xiaoxin Sun, Lisa Zhang, Yuling Wang, Mengying Yu, Minghao Yin & Bangzuo Zhang

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  1. Xiaoxin Sun
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  2. Lisa Zhang
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  3. Yuling Wang
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  4. Mengying Yu
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  5. Minghao Yin
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  6. Bangzuo Zhang
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Correspondence to Minghao Yin.

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Sun, X., Zhang, L., Wang, Y. et al. Attribute-aware deep attentive recommendation. J Supercomput 77, 5510–5527 (2021). https://doi.org/10.1007/s11227-020-03459-9

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