tutorial

Tutorial on cross-domain recommender systems

Authors:

Iván Cantador,

Paolo CremonesiAuthors Info & Claims

RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

Pages 401 - 402

https://doi.org/10.1145/2645710.2645777

Published: 06 October 2014 Publication History

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Abstract

Cross-domain recommender systems aim to generate or enhance personalized recommendations in a target domain by exploiting knowledge (mainly user preferences) from other source domains. This may beneficial for generating better recommendations, e.g. mitigating the cold-start and sparsity problems in a target domain, and enabling personalized cross-selling for items from multiple domains. In this tutorial, we formalize the cross-domain recommendation problem, categorize and survey state of the art cross-domain recommender systems, discuss related evaluation issues, and outline future research directions on the topic.

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Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
Liu WChen CLiao XHu MSu JTan YWang FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645331(334-343)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645331
Jung NHoang VLee OChoi C(2024)Kiosk Recommend System Based on Self-Supervised Representation Learning of User Behaviors in Offline RetailIEEE Internet of Things Journal10.1109/JIOT.2024.336514411:10(18686-18697)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3365144
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Tutorial on cross-domain recommender systems

Recommendations

A cross-domain recommender system with consistent information transfer

Recommender systems provide users with personalized online product and service recommendations and are a ubiquitous part of today's online entertainment smorgasbord. However, many suffer from cold-start problems due to a lack of sufficient preference ...
Domain ranking for cross domain collaborative filtering
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Information & Contributors

Information

Published In

RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

October 2014

458 pages

ISBN:9781450326681

DOI:10.1145/2645710

General Chairs:
Alfred Kobsa
University of California, Irvine
,
Michelle Zhou
IBM
,
Program Chairs:
Martin Ester
Simon Fraser University
,
Yehuda Koren
Google

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 October 2014

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Tutorial

Conference

RecSys'14

Sponsor:

SIGCHI

RecSys'14: Eighth ACM Conference on Recommender Systems

October 6 - 10, 2014

California, Foster City, Silicon Valley, USA

Acceptance Rates

RecSys '14 Paper Acceptance Rate 35 of 234 submissions, 15%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
Liu WChen CLiao XHu MSu JTan YWang FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645331(334-343)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645331
Jung NHoang VLee OChoi C(2024)Kiosk Recommend System Based on Self-Supervised Representation Learning of User Behaviors in Offline RetailIEEE Internet of Things Journal10.1109/JIOT.2024.336514411:10(18686-18697)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3365144
Li HYu LNiu XLeng YDu Q(2023)Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer GateACM Transactions on Knowledge Discovery from Data10.1145/360461518:1(1-28)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3604615
Niu HNguyen DYonekawa KKurokawa MOno CAmagata DMaekawa THara T(2023)User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer LearningProceedings of the 4th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval10.1145/3592571.3592974(37-45)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3592571.3592974
Wu JXie ZYu TZhao HZhang RLi SAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Dynamics-Aware Adaptation for Reinforcement Learning Based Cross-Domain Interactive RecommendationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531969(290-300)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531969
Nanthini MPradeep Mohan Kumar K(2022)TLRNN: Two-Level RNN Based Personalized Recommendation in Tourism DomainCyber Technologies and Emerging Sciences10.1007/978-981-19-2538-2_19(201-211)Online publication date: 30-Aug-2022
https://doi.org/10.1007/978-981-19-2538-2_19
Ogunseyi TAvoussoukpo CJiang Y(2021)Privacy-Preserving Matrix Factorization for Cross-Domain RecommendationIEEE Access10.1109/ACCESS.2021.30914269(91027-91037)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3091426
Ogunseyi TBo TYang C(2021)A privacy-preserving framework for cross-domain recommender systemsComputers and Electrical Engineering10.1016/j.compeleceng.2021.10721393:COnline publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1016/j.compeleceng.2021.107213
Vo NHong MJung J(2020)Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation SystemSensors10.3390/s2009251020:9(2510)Online publication date: 29-Apr-2020
https://doi.org/10.3390/s20092510
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Recommendations

A cross-domain recommender system with consistent information transfer

Domain ranking for cross domain collaborative filtering

Preliminary Investigation of Alleviating User Cold-Start Problem in E-commerce with Deep Cross-Domain Recommender System