Personalized recommendation via cross-domain triadic factorization

Liang HU, Jian CAO, Guandong XU, Longbing CAO, Zhiping GU, Can ZHU

Research output: Chapter in Book/Report/Conference proceedingChapters

208 Citations (Scopus)


Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models. Copyright © 2013 by the International World Wide Web Conferences Steering Committee (IW3C3).

Original languageEnglish
Title of host publicationWWW '13: Proceedings of the 22nd International Conference on World Wide Web
Place of PublicationUSA
PublisherAssociation for Computing Machinery
ISBN (Print)9781450320351
Publication statusPublished - 2013


Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., & Zhu, C. (2013). Personalized recommendation via cross-domain triadic factorization. In WWW '13: Proceedings of the 22nd International Conference on World Wide Web (pp. 595-606). Association for Computing Machinery.


  • Recommender system
  • Cross-domain collaborative filtering
  • Triadic factorization


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