Conference proceeding
Heterogeneous knowledge transfer via domain regularization for improving cross-domain collaborative filtering
2017 IEEE International Conference on Big Data (Big Data), v 2018-, pp 3968-3974
Dec 2017
Abstract
Cross-Domain Collaborative Filtering(CDCF) methods transfer knowledge from auxiliary domains (e.g., books) to improve recommendation in a target domain (e.g. movies). Most CDCF methods exploit homogeneous user feedback, e.g. numeric ratings, from auxiliary domains as the knowledge source. However, in a typical recommender system, the usage data is usually heterogeneous and therefore is potential to better improve recommendation in other domains. In this paper, we propose a novel and generic CDCF solution called Heterogeneous Knowledge Transfer via Domain Regularization (HKT-DR). Our solution is able to mine high quality knowledge from heterogeneous knowledge sources, i.e. both explicit and implicit feedbacks from multiple auxiliary domains, by building a fused user similarity network, and to incorporate the knowledge by imposing domain regularization to constrain matrix factorization objective function. Extensive experiments on real world datasets show that the proposed HKT-DR model outperforms the state-of-the-art CDCF solutions.
Metrics
56 Record Views
3 citations in Web of Science
5 citations in Scopus
Details
- Title
- Heterogeneous knowledge transfer via domain regularization for improving cross-domain collaborative filtering
- Creators
- Yizhou Zang - Drexel UniversityXiaohua Hu - Drexel University, Information Science
- Publication Details
- 2017 IEEE International Conference on Big Data (Big Data), v 2018-, pp 3968-3974
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-85047755031
- Other Identifier
- 991019170155204721