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CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

Authors:

Jianxun Lian,

Fuzheng Zhang,

Xing Xie,

Guangzhong SunAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 817 - 818

https://doi.org/10.1145/3041021.3054207

Published: 03 April 2017 Publication History

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Abstract

To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.

References

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A. M. Elkahky, Y. Song, and X. He. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web, WWW '15, pages 278--288, New York, NY, USA, 2015. ACM.

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[2]

W. Pan, N. N. Liu, E. W. Xiang, and Q. Yang. Transfer learning to predict missing ratings via heterogeneous user feedbacks. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Three, IJCAI'11, pages 2318--2323. AAAI Press, 2011.

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A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '08, pages 650--658, New York, NY, USA, 2008. ACM.

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Cited By

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R N AV. SCh. Vidyadhari Nimmala SRao Gundarapu M(2024)REVOLUTIONIZING TREND RECOMMENDATIONS: A DEEP LEARNING APPROACH FOR IMAGE-BASED INSIGHTSShodhKosh: Journal of Visual and Performing Arts10.29121/shodhkosh.v4.i1.2023.28594:1Online publication date: 30-Jun-2024
https://doi.org/10.29121/shodhkosh.v4.i1.2023.2859
Wu R(2024)RecBERT: Semantic Recommendation Engine with Large Language Model Enhanced Query Segmentation for k-Nearest Neighbors Ranking RetrievalIntelligent and Converged Networks10.23919/ICN.2024.00045:1(42-52)Online publication date: Mar-2024
https://doi.org/10.23919/ICN.2024.0004
Chen JZhang LYang N(2024)Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial TrainingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688116(278-286)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688116
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Index Terms

CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Information & Contributors

Information

Published In

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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Poster

Conference

WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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R N AV. SCh. Vidyadhari Nimmala SRao Gundarapu M(2024)REVOLUTIONIZING TREND RECOMMENDATIONS: A DEEP LEARNING APPROACH FOR IMAGE-BASED INSIGHTSShodhKosh: Journal of Visual and Performing Arts10.29121/shodhkosh.v4.i1.2023.28594:1Online publication date: 30-Jun-2024
https://doi.org/10.29121/shodhkosh.v4.i1.2023.2859
Wu R(2024)RecBERT: Semantic Recommendation Engine with Large Language Model Enhanced Query Segmentation for k-Nearest Neighbors Ranking RetrievalIntelligent and Converged Networks10.23919/ICN.2024.00045:1(42-52)Online publication date: Mar-2024
https://doi.org/10.23919/ICN.2024.0004
Chen JZhang LYang N(2024)Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial TrainingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688116(278-286)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688116
Hu BWang WWang SLiu ZShen BHe YChen JSerra ESpezzano F(2024)The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck PerspectiveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679595(880-889)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679595
Hao BYang CGuo LYu JYin HAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Motif-based Prompt Learning for Universal Cross-domain RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635754(257-265)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635754
Li YHou LLi J(2024)Towards Knowledge-Aware and Deep Reinforced Cross-Domain Recommendation Over Collaborative Knowledge GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339126836:11(7171-7187)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3391268
Kong MHou MZhao SLiu FSu RChen Y(2024)DADINExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122880243:COnline publication date: 25-Jun-2024
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Zang TZhu YZhang RZhu JTang F(2024)Explicitly modeling relationships between domain-specific and domain-invariant interests for cross-domain recommendationWorld Wide Web10.1007/s11280-024-01305-z27:6Online publication date: 28-Oct-2024
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Gheewala SXu SYeom S(2024)Deep shared learning and attentive domain mapping for cross-domain recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-024-09416-yOnline publication date: 27-Sep-2024
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Zaman NJana A(2024)Cross domain recommendation using dual inductive transfer learningMultimedia Tools and Applications10.1007/s11042-024-19967-2Online publication date: 8-Aug-2024
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