Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Social media recommendation based on people and tags
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Interactive recommendations in social endorsement networks
Proceedings of the fourth ACM conference on Recommender systems
PointBurst: towards a trust-relationship framework for improved social recommendations
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
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Social networking services possess two features: (1) capturing the social relationships among people, represented by the social network, and (2) allowing users to express their preferences on different kinds of items (e.g. photo, celebrity, pages) through endorsing buttons, represented by a kind of endorsement bipartite graph. In this work, using such information, we propose a novel recommendation method, which leverages the viral marketing in the social network and the wisdom of crowds from endorsement network. Our recommendation consists of two parts. First, given some query terms describing user's preference, we find a set of targeted influencers who have the maximum activation probability on those nodes related to the query terms in the social network. Second, based on the derived targeted influencers as key experts, we recommend items via the endorsement network. We conduct the experiments on DBLP co-authorship social network with author-reference data as the endorsement network. The results show our method can achieve effective recommendations.