Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the third ACM international conference on Web search and data mining
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
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The popularity of social networking greatly increases interaction among people. However, one major challenge remains --- how to connect people who share similar interests. In a social network, the majority of people who share similar interests with given a user are in the long tail that accounts for 80% of total population. Searching for similar users by following links in social network has two limitations: it is inefficient and incomplete. Thus, it is desirable to design new methods to find like-minded people. In this paper, we propose to use collective wisdom from the crowd or tag networks to solve the problem. In a tag network, each node represents a tag as described by some words, and the weight of an undirected edge represents the co-occurrence of two tags. As such, the tag network describes the semantic relationships among tags. In order to connect to other users of similar interests via a tag network, we use diffusion kernels on the tag network to measure the similarity between pairs of tags. The similarity of people's interests are measured on the basis of similar tags they share. To recommend people who are alike, we retrieve top k people sharing the most similar tags. Compared to two baseline methods triadic closure and LSI, the proposed tag network approach achieves 108% and 27% relative improvements on the BlogCatalog dataset, respectively.