research-article

Attribute-aware deep attentive recommendation

Authors:

Bangzuo ZhangAuthors Info & Claims

The Journal of Supercomputing, Volume 77, Issue 6

Pages 5510 - 5527

https://doi.org/10.1007/s11227-020-03459-9

Published: 01 June 2021 Publication History

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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Cited By

Zheng JChen SCao FPeng FHuang M(2024)Explainable recommendation based on fusion representation of multi-type feature embeddingThe Journal of Supercomputing10.1007/s11227-023-05831-x80:8(10370-10393)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05831-x
Ma RLiu NYuan JYang HZhang J(2022)CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce EnvironmentProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546773(278-287)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546773
Wang SMa ZSun XZhao HWei XMa RTang B(2022)Leveraging side information as adjusting embedding to improve user representation for recommendationsThe Journal of Supercomputing10.1007/s11227-022-04635-978:17(19322-19345)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11227-022-04635-9

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cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 77, Issue 6

Jun 2021

1189 pages

ISSN:0920-8542

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© Springer Science+Business Media, LLC, part of Springer Nature 2020.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2021

Accepted: 14 October 2020

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Cited By

Zheng JChen SCao FPeng FHuang M(2024)Explainable recommendation based on fusion representation of multi-type feature embeddingThe Journal of Supercomputing10.1007/s11227-023-05831-x80:8(10370-10393)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05831-x
Ma RLiu NYuan JYang HZhang J(2022)CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce EnvironmentProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546773(278-287)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546773
Wang SMa ZSun XZhao HWei XMa RTang B(2022)Leveraging side information as adjusting embedding to improve user representation for recommendationsThe Journal of Supercomputing10.1007/s11227-022-04635-978:17(19322-19345)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11227-022-04635-9

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