research-article

A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression

Authors:

Man-Kwan ShanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 6

Article No.: 67, Pages 1 - 26

https://doi.org/10.1145/3231601

Published: 01 November 2018 Publication History

Abstract

Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users’ time is saved and sellers’ profits are increased. Cross-domain recommender systems aim to recommend items based on users’ different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains’ rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory.

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Cited By

Tang RYang C(2024)A Cross-Domain Latent Topic Model for Item Tagging and Recommendation Systems2024 International Conference on Culture-Oriented Science & Technology (CoST)10.1109/CoST64302.2024.00080(373-378)Online publication date: 25-Aug-2024
https://doi.org/10.1109/CoST64302.2024.00080
Patel RThakkar P(2024)IBCFaiCDR: Auxiliary data-driven item-based collaborative filtering in cross-domain RSs to address user cold start problemResults in Engineering10.1016/j.rineng.2024.10325724(103257)Online publication date: Dec-2024
https://doi.org/10.1016/j.rineng.2024.103257
Mezni HSellami MAl‐Rasheed AElmannai H(2024)Cross‐network service recommendation in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.806336:13Online publication date: 18-Mar-2024
https://doi.org/10.1002/cpe.8063
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Index Terms

A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Decision support systems
      1. Data analytics

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 6

Regular Papers

November 2018

290 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3289398

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2018 ACM.

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Publication History

Published: 01 November 2018

Accepted: 01 June 2018

Revised: 01 April 2018

Received: 01 November 2017

Published in TIST Volume 9, Issue 6

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Ministry of Science and Technology of Taiwan (MOST)
Academia Sinica

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Cited By

Tang RYang C(2024)A Cross-Domain Latent Topic Model for Item Tagging and Recommendation Systems2024 International Conference on Culture-Oriented Science & Technology (CoST)10.1109/CoST64302.2024.00080(373-378)Online publication date: 25-Aug-2024
https://doi.org/10.1109/CoST64302.2024.00080
Patel RThakkar P(2024)IBCFaiCDR: Auxiliary data-driven item-based collaborative filtering in cross-domain RSs to address user cold start problemResults in Engineering10.1016/j.rineng.2024.10325724(103257)Online publication date: Dec-2024
https://doi.org/10.1016/j.rineng.2024.103257
Mezni HSellami MAl‐Rasheed AElmannai H(2024)Cross‐network service recommendation in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.806336:13Online publication date: 18-Mar-2024
https://doi.org/10.1002/cpe.8063
Yi MLiu MFeng CDeng W(2023)A cross-domain recommendation model by unified modelling high-order information and rating informationJournal of Information Science10.1177/01655515231182068Online publication date: 8-Jul-2023
https://doi.org/10.1177/01655515231182068
Liow CLi CYang CLin SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Pseudo Triplet Networks for Classification Tasks with Cross-Source Feature IncompletenessProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615154(4079-4083)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615154
Jiang HLi CCai JTian RWang JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615053(945-954)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615053
Zhang QLiao WZhang GYuan BLu J(2023)A Deep Dual Adversarial Network for Cross-Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313295335:4(3266-3278)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3132953
Yuan HHernandez A(2023)User Cold Start Problem in Recommendation Systems: A Systematic ReviewIEEE Access10.1109/ACCESS.2023.333870511(136958-136977)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3338705
Chen YZhang XZou QWang HHuang SLu L(2022)Multivariate Modeling Analysis Based on Partial Least Squares Regression and Principal Component RegressionProceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing10.1145/3523286.3524584(442-446)Online publication date: 21-Jan-2022
https://dl.acm.org/doi/10.1145/3523286.3524584
Guan RPang HGiunchiglia FLiang YFeng X(2022)Cross-Domain Meta-Learner for Cold-Start RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3208005(1-16)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3208005
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