research-article

Signed Distance-based Deep Memory Recommender

Authors:

Xiangnan KongAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 1841 - 1852

https://doi.org/10.1145/3308558.3313460

Published: 13 May 2019 Publication History

Abstract

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

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Published: 13 May 2019

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May 13 - 17, 2019

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https://dl.acm.org/doi/10.1145/3680552
Liu CZehmakan AZhang ZBaeza-Yates RBonchi F(2024)Fast Query of Biharmonic Distance in NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671856(1887-1897)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671856
Kontogianni AAlepis EVirvou MPatsakis CKontogianni AAlepis EVirvou MPatsakis C(2024)Artificial Intelligence in Smart TourismSmart Tourism–The Impact of Artificial Intelligence and Blockchain10.1007/978-3-031-50883-7_5(75-85)Online publication date: 2-Feb-2024
https://doi.org/10.1007/978-3-031-50883-7_5
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https://doi.org/10.1007/978-3-031-50883-7_2
Zhao WLin ZFeng ZWang PWen J(2022)A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation AlgorithmsACM Transactions on Information Systems10.1145/354579641:2(1-41)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3545796
Hu YLiu YMiao CMiao YAl Hasan MXiong L(2022)Memory Bank Augmented Long-tail Sequential RecommendationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557391(791-801)Online publication date: 17-Oct-2022
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Barkan OFuchs YCaciularu AKoenigstein N(2020)Explainable Recommendations via Attentive Multi-Persona Collaborative FilteringProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412226(468-473)Online publication date: 22-Sep-2020
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