Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Yes, there is a correlation: - from social networks to personal behavior on the web
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
Proceedings of the forty-first annual ACM symposium on Theory of computing
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A collaborative filtering approach to ad recommendation using the query-ad click graph
Proceedings of the 18th ACM conference on Information and knowledge management
Classification-enhanced ranking
Proceedings of the 19th international conference on World wide web
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Display advertising impact: search lift and social influence
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale behavioral targeting with a social twist
Proceedings of the 20th ACM international conference on Information and knowledge management
How user behavior is related to social affinity
Proceedings of the fifth ACM international conference on Web search and data mining
Social influence in social advertising: evidence from field experiments
Proceedings of the 13th ACM Conference on Electronic Commerce
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Link structure in online networks carries varying semantics. For example, Facebook links carry social semantics while LinkedIn links carry professional semantics. It has been shown that online networks are useful for predicting users' future activities. In this paper, we introduce a new related problem: given a collection of networks, how can we determine the relative importance of each network for predicting user activities? We propose a framework that allows us to quantify the relative predictive value of each network in a setting where multiple networks are available. We give an ɛ-net algorithm to solve the problem and prove that it finds a solution that is arbitrarily close to the optimal solution. Experimentally, we focus our study on the prediction of ad clicks, where it is already known that a single social network improves prediction. The networks we study are implicit affiliations networks, which are based on users' browsing history rather than declared relationships between the users. We create two networks based on covisitation to pages in the Facebook domain and Wikipedia domain. The learned relative weighting of these networks demonstrates covisitation networks are indeed useful for prediction, but that no single network is predictive of all kinds of ads. Rather, each category of ads calls for a significantly different weighting of these networks.