research-article

A Survey on Heterogeneous One-class Collaborative Filtering

Authors:

Zhong MingAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 38, Issue 4

Article No.: 35, Pages 1 - 54

https://doi.org/10.1145/3402521

Published: 11 August 2020 Publication History

Abstract

Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users’ preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging compared with that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g., purchases) with the assistance of the auxiliary feedback (e.g., examinations). In this survey, we provide an overview of the representative HOCCF methods from the perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorization-based methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions.

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Yang EPan WYang QMing Z(2024)Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative FilteringACM Transactions on Information Systems10.1145/365285342:5(1-50)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652853
Pardo EValdiviezo-Diaz PBarba-Guaman LChicaiza J(2024)Collaborative Filtering Recommendation Systems Based on Deep Learning: An Experimental StudyGood Practices and New Perspectives in Information Systems and Technologies10.1007/978-3-031-60218-4_6(54-63)Online publication date: 13-May-2024
https://doi.org/10.1007/978-3-031-60218-4_6
Rao QLiu YPan WMing Z(2023)BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608781(625-636)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608781
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Index Terms

A Survey on Heterogeneous One-class Collaborative Filtering
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 38, Issue 4

October 2020

375 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3402434

Editor:
Min Zhang
Tsinghua University, China

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Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2020

Accepted: 01 May 2020

Revised: 01 April 2020

Received: 01 October 2019

Published in TOIS Volume 38, Issue 4

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Yang EPan WYang QMing Z(2024)Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative FilteringACM Transactions on Information Systems10.1145/365285342:5(1-50)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652853
Pardo EValdiviezo-Diaz PBarba-Guaman LChicaiza J(2024)Collaborative Filtering Recommendation Systems Based on Deep Learning: An Experimental StudyGood Practices and New Perspectives in Information Systems and Technologies10.1007/978-3-031-60218-4_6(54-63)Online publication date: 13-May-2024
https://doi.org/10.1007/978-3-031-60218-4_6
Rao QLiu YPan WMing Z(2023)BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608781(625-636)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608781
Liu YRao QPan WMing Z(2023)Variational Collective Graph AutoEncoder for Multi-behavior Recommendation2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00053(438-447)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00053
Luo JHe MPan WMing Z(2023)BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-2100-y17:5Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11704-022-2100-y
Zhang JWang LMa YJiang QWang B(2023) Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE IEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2385518:8(1303-1316)Online publication date: 23-Jul-2023
https://doi.org/10.1002/tee.23855
Khoali MLaaziz YTali ASalaudeen H(2022)A Survey of One Class E-Commerce Recommendation System TechniquesElectronics10.3390/electronics1106087811:6(878)Online publication date: 10-Mar-2022
https://doi.org/10.3390/electronics11060878
Lee CHan DHan KYi M(2022)Improving Graph-Based Movie Recommender System Using Cinematic ExperienceApplied Sciences10.3390/app1203149312:3(1493)Online publication date: 29-Jan-2022
https://doi.org/10.3390/app12031493
Luo JHe MLin XPan WMing ZAl Hasan MXiong L(2022)Dual-Task Learning for Multi-Behavior Sequential RecommendationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557298(1379-1388)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557298
Ma WChen XPan WMing ZSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)VAE++Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498436(666-674)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498436
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