Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
ReCoM: reinforcement clustering of multi-type interrelated data objects
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Star-Structured High-Order Heterogeneous Data Co-clustering Based on Consistent Information Theory
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Improving one-class collaborative filtering by incorporating rich user information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Matrix co-factorization for recommendation with rich side information and implicit feedback
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Bayesian latent variable models for collaborative item rating prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Comparing apples to oranges: a scalable solution with heterogeneous hashing
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Using emotional context from article for contextual music recommendation
Proceedings of the 21st ACM international conference on Multimedia
Supporting exploratory people search: a study of factor transparency and user control
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the first ACM conference on Online social networks
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Social networks enable users to create different types of personal items. In dealing with serious information overload, the major problems of social recommendation are sparsity and cold start. In existing approaches, relational and heterogeneous domains can not be effectively utilized for social recommendation, which brings a challenge to model users and multiple types of items together on social networks. In this paper, we consider how to represent social networks with multiple relational domains and alleviate the major problems in an individual domain by transferring knowledge from other domains. We propose a novel Hybrid Random Walk (HRW), which can integrate multiple heterogeneous domains including directed/undirected links, signed/unsigned links and within-domain/cross-domain links into a star-structured hybrid graph with user graph at the center. We perform random walk until convergence and use the steady state distribution for recommendation. We conduct experiments on a real social network dataset and show that our method can significantly outperform existing social recommendation approaches.