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

Authors:

Daichi Amagata,

Takuya Maekawa,

Mori KurokawaAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 398 - 403

https://doi.org/10.1145/3308560.3316596

Published: 13 May 2019 Publication History

Abstract

Many current applications use recommender systems to predict user preferences, aiming at improving user experience and increasing the amount of sales and the usage time that users spent on the application. However, it is not an easy task to recommend items to new users accurately because of the user cold-start problem, which means that recommendation performance will degrade on users with little interaction, particularly for latent users who have never used the service before. In this work, we combine an online shopping domain with information from an ad platform, and then apply deep learning to build a cross-domain recommender system based on shared users of these two domains, to alleviate the user cold-start problem. Experimental results show the effectiveness of our deep cross-domain recommender system on handling user cold-start problem. By our framework, it is possible to recommend products to users of other domain through ad distribution in a more accurate level, and to increase sales amount of online shopping.

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Cited By

Zhang YShirakawa MHara T(2024)Word-Level Political Sentiments Inferred From Social Media and Application in Recommendation DiversificationACM Transactions on the Web10.1145/370064319:1(1-26)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3700643
Zhao SZhang YLi MPiao XYin B(2024)Contextual Semantics Interaction Graph Embedding Learning for Recommender SystemsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339470111:5(6333-6346)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3394701
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
Show More Cited By

Index Terms

Preliminary Investigation of Alleviating User Cold-Start Problem in E-commerce with Deep Cross-Domain Recommender System
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems

Index terms have been assigned to the content through auto-classification.

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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 ...

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Published In

cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 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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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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

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Cited By

Zhang YShirakawa MHara T(2024)Word-Level Political Sentiments Inferred From Social Media and Application in Recommendation DiversificationACM Transactions on the Web10.1145/370064319:1(1-26)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3700643
Zhao SZhang YLi MPiao XYin B(2024)Contextual Semantics Interaction Graph Embedding Learning for Recommender SystemsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339470111:5(6333-6346)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3394701
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
Hirata KAmagata DFujita SHara T(2023)Categorical Diversity-Aware Inner Product SearchIEEE Access10.1109/ACCESS.2023.323407211(2586-2596)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3234072
Li ZAmagata DZhang YHara THaruta SYonekawa KKurokawa M(2023)Semantic Relation Transfer for Non-overlapped Cross-domain RecommendationsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33380-4_21(271-283)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33380-4_21
Viktoratos ITsadiras A(2022)A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising SystemsAlgorithms10.3390/a1503007215:3(72)Online publication date: 22-Feb-2022
https://doi.org/10.3390/a15030072
Chen XLei CLiu DWang GTang HZha ZLi H(2022)E-Commerce Storytelling Recommendation Using Attentional Domain-Transfer Network and Adversarial Pre-TrainingIEEE Transactions on Multimedia10.1109/TMM.2021.305452524(506-518)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3054525
Shin MCho YKang HKim M(2022)Meta-learning-based lightweight learning framework for healthcare recommendation system2022 13th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC55196.2022.9952713(1107-1109)Online publication date: 19-Oct-2022
https://doi.org/10.1109/ICTC55196.2022.9952713
Li ZAmagata DZhang YMaekawa THara TYonekawa KKurokawa M(2022)HML4RecKnowledge-Based Systems10.1016/j.knosys.2022.109674255:COnline publication date: 14-Nov-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109674
Murao JYonekawa KKurokawa MAmagata DMaekawa THara T(2022)Concept Drift Detection with Denoising Autoencoder in Incomplete DataMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_35(541-552)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_35
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