The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-Domain Mediation in Collaborative Filtering
UM '07 Proceedings of the 11th international conference on User Modeling
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multilevel and Longitudinal Modeling Using Stata, Second Edition
Multilevel and Longitudinal Modeling Using Stata, Second Edition
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Cross-Domain Collaborative Filtering: A Brief Survey
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
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Cross-domain collaborative filtering (CDCF), which aims to leverage data from multiple domains to relieve the data sparsity issue, is becoming an emerging research topic in recent years. However, current CDCF methods that mainly consider user and item factors but largely neglect the heterogeneity of domains may lead to improper knowledge transfer issues. To address this problem, we propose a novel CDCF model, the Bilinear Multilevel Analysis (BLMA), which seamlessly introduces multilevel analysis theory to the most successful collaborative filtering method, matrix factorization (MF). Specifically, we employ BLMA to more efficiently address the determinants of ratings from a hierarchical view by jointly considering domain, community, and user effects so as to overcome the issues caused by traditional MF approaches. Moreover, a parallel Gibbs sampler is provided to learn these effects. Finally, experiments conducted on a realworld dataset demonstrate the superiority of the BLMA over other state-of-the-art methods.