Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
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
Multi-relational Link Prediction in Heterogeneous Information Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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Social recommendation methods, often taking only one kind of relationship in social network into consideration, still faces the data sparsity and cold-start user problems. This paper presents a novel recommendation method based on multi-relational analysis: first, combine different relation networks by applying optimal linear regression analysis, and then, based on the optimal network combination, put forward a recommendation algorithm combined with multi-relational social network. The experimental results on Epinions dataset indicate that, compared with existing algorithms, can effectively alleviate data sparsity as well as cold-start issues, and achieve better performance.