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Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines

Authors:

Roberto Pagano,

Alan HanjalicAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 2

Article No.: 15, Pages 1 - 23

https://doi.org/10.1145/3291756

Published: 13 February 2019 Publication History

Abstract

User interactions can be considered to constitute different feedback channels, for example, view, click, like or follow, that provide implicit information on users’ preferences. Each implicit feedback channel typically carries a unary, positive-only signal that can be exploited by collaborative filtering models to generate lists of personalized recommendations. This article investigates how a learning-to-rank recommender system can best take advantage of implicit feedback signals from multiple channels. We focus on Factorization Machines (FMs) with Bayesian Personalized Ranking (BPR), a pairwise learning-to-rank method, that allows us to experiment with different forms of exploitation. We perform extensive experiments on three datasets with multiple types of feedback to arrive at a series of insights. We compare conventional, direct integration of feedback types with our proposed method, which exploits multiple feedback channels during the sampling process of training. We refer to our method as multi-channel sampling. Our results show that multi-channel sampling outperforms conventional integration, and that sampling with the relative “level” of feedback is always superior to a level-blind sampling approach. We evaluate our method experimentally on three datasets in different domains and observe that with our multi-channel sampler the accuracy of recommendations can be improved considerably compared to the state-of-the-art models. Further experiments reveal that the appropriate sampling method depends on particular properties of datasets such as popularity skewness.

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Cited By

Zhou TYe HCao F(2024)Node-personalized multi-graph convolutional networks for recommendationNeural Networks10.1016/j.neunet.2024.106169173(106169)Online publication date: May-2024
https://doi.org/10.1016/j.neunet.2024.106169
Khan ZChaudhary NAbbasi WLing SRaja M(2023)Design of Confidence-Integrated Denoising Auto-Encoder for Personalized Top-N Recommender SystemsMathematics10.3390/math1103076111:3(761)Online publication date: 2-Feb-2023
https://doi.org/10.3390/math11030761
Gong JWan YLiu YLi XZhao YWang CLin YFang XFeng WZhang JTang J(2023)Reinforced MOOCs Concept Recommendation in Heterogeneous Information NetworksACM Transactions on the Web10.1145/358051017:3(1-27)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3580510
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Index Terms

Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
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. Collaborative filtering

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 2

April 2019

410 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3306215

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2019

Accepted: 01 October 2018

Revised: 01 October 2018

Received: 01 November 2017

Published in TOIS Volume 37, Issue 2

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Cited By

Zhou TYe HCao F(2024)Node-personalized multi-graph convolutional networks for recommendationNeural Networks10.1016/j.neunet.2024.106169173(106169)Online publication date: May-2024
https://doi.org/10.1016/j.neunet.2024.106169
Khan ZChaudhary NAbbasi WLing SRaja M(2023)Design of Confidence-Integrated Denoising Auto-Encoder for Personalized Top-N Recommender SystemsMathematics10.3390/math1103076111:3(761)Online publication date: 2-Feb-2023
https://doi.org/10.3390/math11030761
Gong JWan YLiu YLi XZhao YWang CLin YFang XFeng WZhang JTang J(2023)Reinforced MOOCs Concept Recommendation in Heterogeneous Information NetworksACM Transactions on the Web10.1145/358051017:3(1-27)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3580510
Liu WZhang ZBai YLiu YYang ALi J(2023)A DeepFM-Based Non-Parametric Model Enabled Big Data Platform for Predicting Passenger Car Sales in Sustainable WayIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330342824:12(16018-16028)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3303428
Liu HYang BLi D(2023)Graph Collaborative Filtering Based on Dual-Message Propagation MechanismIEEE Transactions on Cybernetics10.1109/TCYB.2021.310052153:1(352-364)Online publication date: Jan-2023
https://doi.org/10.1109/TCYB.2021.3100521
Xu YZhu YYu J(2021)Modeling Multiple Coexisting Category-Level Intentions for Next Item RecommendationACM Transactions on Information Systems10.1145/344164239:3(1-24)Online publication date: 5-May-2021
https://dl.acm.org/doi/10.1145/3441642
Adomavicius GBockstedt JCurley SZhang J(2021)Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference RatingsACM Transactions on Information Systems10.1145/343002839:2(1-38)Online publication date: 14-Jan-2021
https://dl.acm.org/doi/10.1145/3430028
Zhang QSato MTakemori SOhkuma THung CHong JBechini ASong E(2021)Incorporating multi-level positive feedback to session-based nearest-neighborProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3442012(1371-1374)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3442012
Zafar Ali Khan NMahalakshmi R(2021)Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit ReviewsJournal of Interconnection Networks10.1142/S021926592141013922:Supp01Online publication date: 11-Sep-2021
https://doi.org/10.1142/S0219265921410139
Yang MXu S(2021)A novel deep quantile matrix completion model for top-N recommendationKnowledge-Based Systems10.1016/j.knosys.2021.107302(107302)Online publication date: Jul-2021
https://doi.org/10.1016/j.knosys.2021.107302
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