DTCDR

a framework for Dual-Target Cross-Domain Recommendation

Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, Xiaolin Zheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain with sparser information. However, each of the two domains may be relatively richer in certain types of information (e.g., ratings, reviews, user profiles, item details, and tags), and thus, if we can leverage such information well, it is possible to improve the recommendation performance on both domains simultaneously (i.e., dual-target CDR), rather than a single target domain only. To this end, in this paper, we propose a new framework, DTCDR, for Dual-Target Cross-Domain Recommendation. In DTCDR, we first extensively utilize rating and multi-source content information to generate rating and document embeddings of users and items. Then, based on Multi-Task Learning (MTL), we design an adaptable embedding-sharing strategy to combine and share the embeddings of common users across domains, with which DTCDR can improve the recommendation performance on both richer and sparser (i.e., dual-target) domains simultaneously. Extensive experiments conducted on real-world datasets demonstrate that DTCDR can significantly improve the recommendation accuracies on both richer and sparser domains and outperform the state-of-the-art single-domain and cross-domain approaches.
Original languageEnglish
Title of host publicationCIKM '19
Subtitle of host publicationProceedings of the 28th ACM International Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1533-1542
Number of pages10
ISBN (Electronic)9781450369763
DOIs
Publication statusPublished - 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period3/11/197/11/19

Keywords

  • Recommender Systems
  • Cross-Domain Recommendation
  • Collaborative Filtering
  • Multi-task Learning

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  • Cite this

    Zhu, F., Chen, C., Wang, Y., Liu, G., & Zheng, X. (2019). DTCDR: a framework for Dual-Target Cross-Domain Recommendation. In CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1533-1542). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3357384.3357992