GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Studying the use of popular destinations to enhance web search interaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction of Information Diffusion Probabilities for Independent Cascade Model
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
Proceedings of the sixth ACM international conference on Web search and data mining
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In this paper we present a novel graph-based data abstraction for modeling the browsing behavior of web users. The objective is to identify users who discover interesting pages before others. We call these users early adopters. By tracking the browsing activity of early adopters we can identify new interesting pages early, and recommend these pages to similar users. We focus on news and blog pages, which are more dynamic in nature and more appropriate for recommendation. Our proposed model is called early-adopter graph. In this graph, nodes represent users and a directed arc between users u and v expresses the fact that u and v visit similar pages and, in particular, that user u tends to visit those pages before user v. The weight of the edge is the degree to which the temporal rule "v visits a page before v" holds. Based on the early-adopter graph, we build a recommendation system for news and blog pages, which outperforms other out-of-the-shelf recommendation systems based on collaborative filtering.