Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Personalized Popular Blog Recommender Service for Mobile Applications
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized search on Flickr based on searcher's preference prediction
Proceedings of the 20th international conference companion on World wide web
ExpertiseNet: relational and evolutionary expert modeling
UM'05 Proceedings of the 10th international conference on User Modeling
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People are alike their graphic neighbors in social networks, which is generally accepted as the basic assumption in interest prediction. But what kind of neighbors is a better information source? This paper aims at answering this question by comparing the results of predicting users' interests in blog social networks with different relationships and parameters. Since social networks usually keep "friends" and "visitors" as basic social roles, we take these two online social relationships as the main information source. In this paper, we discover that (1) combining different information sources might lead to better prediction, and (2) there are many other factors that can affect the results significantly.