research-article

Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation

Authors:

Shaoping MaAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 127 - 136

https://doi.org/10.1145/3269206.3271710

Published: 17 October 2018 Publication History

Abstract

Nowadays, recommender systems provide essential web services on the Internet. There are mainly two categories of traditional recommendation algorithms: Content-Based (CB) and Collaborative Filtering (CF). CF methods make recommendations mainly according to the historical feedback information. They usually perform better when there is sufficient feedback information but less successful on new users and items, which is called the "cold-start'' problem. However, CB methods help in this scenario because of using content information. To take both advantages of CF and CB, how to combine them is a challenging issue. To the best of our knowledge, little previous work has been done to solve the problem in one unified recommendation model. In this work, we study how to integrate CF and CB, which utilizes both types of information in model-level but not in result-level and makes recommendations adaptively. A novel attention-based model named Attentional Content&Collaborate Model (ACCM) is proposed. Attention mechanism helps adaptively adjust for each user-item pair from which source information the recommendation is made. Especially, a "cold sampling'' learning strategy is designed to handle the cold-start problem. Experimental results on two benchmark datasets show that the ACCM performs better on both warm and cold tests compared to the state-of-the-art algorithms.

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Guo JYin ZFeng SYao DLiu S(2025)Dual intent view contrastive learning for knowledge aware recommender systemsScientific Reports10.1038/s41598-025-86416-x15:1Online publication date: 16-Jan-2025
https://doi.org/10.1038/s41598-025-86416-x
Li NGuo BLiu YDing YYao LFan XYu Z(2024)Hierarchical Constrained Variational Autoencoder for interaction-sparse recommendationsInformation Processing & Management10.1016/j.ipm.2024.10364161:3(103641)Online publication date: May-2024
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Shirali AKazemi RAmini A(2024)Collaborative Filtering With Representation Learning in the Frequency DomainInformation Sciences10.1016/j.ins.2024.121240(121240)Online publication date: Jul-2024
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Index Terms

Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

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Published: 17 October 2018

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Natural Science Foundation of China
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October 22 - 26, 2018

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Cited By

Guo JYin ZFeng SYao DLiu S(2025)Dual intent view contrastive learning for knowledge aware recommender systemsScientific Reports10.1038/s41598-025-86416-x15:1Online publication date: 16-Jan-2025
https://doi.org/10.1038/s41598-025-86416-x
Li NGuo BLiu YDing YYao LFan XYu Z(2024)Hierarchical Constrained Variational Autoencoder for interaction-sparse recommendationsInformation Processing & Management10.1016/j.ipm.2024.10364161:3(103641)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103641
Shirali AKazemi RAmini A(2024)Collaborative Filtering With Representation Learning in the Frequency DomainInformation Sciences10.1016/j.ins.2024.121240(121240)Online publication date: Jul-2024
https://doi.org/10.1016/j.ins.2024.121240
Singh NSingh S(2024)A systematic literature review of solutions for cold start problemInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02359-y15:7(2818-2852)Online publication date: 14-May-2024
https://doi.org/10.1007/s13198-024-02359-y
Zheng JJing TCao FKang YChen QLi Y(2023)A Multiscale Neighbor-Aware Attention Network for Collaborative FilteringElectronics10.3390/electronics1220437212:20(4372)Online publication date: 22-Oct-2023
https://doi.org/10.3390/electronics12204372
Wang WLin XWang LFeng FWei YChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Equivariant Learning for Out-of-Distribution Cold-start RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612522(903-914)Online publication date: 27-Oct-2023
https://doi.org/10.1145/3581783.3612522
Wu CLian DGe YZhu ZChen E(2023)Influence-Driven Data Poisoning for Robust Recommender SystemsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.3274759(1-17)Online publication date: 2023
https://doi.org/10.1109/TPAMI.2023.3274759
Yue GXiao RZhao ZLi C(2023)AF-GCN: Attribute-Fusing Graph Convolution Network for RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2022.31925989:2(597-607)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3192598
Lee WCho Y(2023)A Flexible Two-Tower Model for Item Cold-Start RecommendationIEEE Access10.1109/ACCESS.2023.334691811(146194-146207)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3346918
Yan BWang HOuyang ZChen CXia Y(2023)Item Attribute-Aware Contrastive Learning for Sequential RecommendationIEEE Access10.1109/ACCESS.2023.329383911(70795-70804)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3293839
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