research-article

Discrete Deep Learning for Fast Content-Aware Recommendation

Authors:

Defu LianAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 717 - 726

https://doi.org/10.1145/3159652.3159688

Published: 02 February 2018 Publication History

Abstract

Cold-start problem and recommendation efficiency have been regarded as two crucial challenges in the recommender system. In this paper, we propose a hashing based deep learning framework called Discrete Deep Learning (DDL), to map users and items to Hamming space, where a user»s preference for an item can be efficiently calculated by Hamming distance, and this computation scheme significantly improves the efficiency of online recommendation. Besides, DDL unifies the user-item interaction information and the item content information to overcome the issues of data sparsity and cold-start. To be more specific, to integrate content information into our DDL framework, a deep learning model, Deep Belief Network (DBN), is applied to extract effective item representation from the item content information. Besides, the framework imposes balance and irrelevant constraints on binary codes to derive compact but informative binary codes. Due to the discrete constraints in DDL, we propose an efficient alternating optimization method consisting of iteratively solving a series of mixed-integer programming subproblems. Extensive experiments have been conducted to evaluate the performance of our DDL framework on two different Amazon datasets, and the experimental results demonstrate the superiority of DDL over the state-of-the-art methods regarding online recommendation efficiency and cold-start recommendation accuracy.

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https://doi.org/10.1109/TNNLS.2024.3387757
Di YShi HMa RGao HLiu YWang W(2024)FedRL: A Reinforcement Learning Federated Recommender System for Efficient Communication Using Reinforcement Selector and Hypernet GeneratorACM Transactions on Recommender Systems10.1145/3682076Online publication date: 29-Jul-2024
https://doi.org/10.1145/3682076
Zhang YWang ZYu WHu LJiang PGai KChen X(2024)Soft Contrastive Sequential RecommendationACM Transactions on Information Systems10.1145/366532542:6(1-28)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3665325
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Index Terms

Discrete Deep Learning for Fast Content-Aware Recommendation
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
    2. Data mining
  2. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Conferences

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

Copyright © 2018 ACM.

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Published: 02 February 2018

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Fundamental Research Funds for the Central Universities
Australian Research Council
National Natural Science Foundation of China

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WSDM 2018

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WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhang HZhou XShen ZLi Y(2025)PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash EmbeddingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338775736:1(32-46)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2024.3387757
Di YShi HMa RGao HLiu YWang W(2024)FedRL: A Reinforcement Learning Federated Recommender System for Efficient Communication Using Reinforcement Selector and Hypernet GeneratorACM Transactions on Recommender Systems10.1145/3682076Online publication date: 29-Jul-2024
https://doi.org/10.1145/3682076
Zhang YWang ZYu WHu LJiang PGai KChen X(2024)Soft Contrastive Sequential RecommendationACM Transactions on Information Systems10.1145/366532542:6(1-28)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3665325
Monteil JVaskovych VLu WMajumder Avan den Hengel A(2024)MARec: Metadata Alignment for cold-start RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688125(401-410)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688125
Xu YZhu LLi JLi FShen H(2024)Temporal Social Graph Network Hashing for Efficient RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.3352255(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2024.3352255
Farshidi SRezaee KMazaheri SRahimi ADadashzadeh AZiabakhsh MEskandari SJansen S(2024)Understanding user intent modeling for conversational recommender systems: a systematic literature reviewUser Modeling and User-Adapted Interaction10.1007/s11257-024-09398-xOnline publication date: 6-Jun-2024
https://doi.org/10.1007/s11257-024-09398-x
Luo FWu JWang T(2023)Discrete Listwise Content-aware RecommendationACM Transactions on Knowledge Discovery from Data10.1145/360933418:1(1-20)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3609334
Xia XYu JWang QYang CHung NYin H(2023)Efficient On-Device Session-Based RecommendationACM Transactions on Information Systems10.1145/358036441:4(1-24)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1145/3580364
Zhang HLuo FWu JHe XLi Y(2023)LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix FactorizationACM Transactions on Information Systems10.1145/357836141:4(1-28)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1145/3578361
Wu HLi NKwok KCai XZhang JLong JK. Ng M(2023)Feature Matching Machine for Cold-Start RecommendationIEEE Transactions on Services Computing10.1109/TSC.2023.3334241(1-15)Online publication date: 2023
https://doi.org/10.1109/TSC.2023.3334241
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