GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
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Mining knowledge-sharing sites for viral marketing
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Mining newsgroups using networks arising from social behavior
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Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Propagation of trust and distrust
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Information diffusion through blogspace
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Identifying early buyers from purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
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Modeling and predicting personal information dissemination behavior
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Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Information flow modeling based on diffusion rate for prediction and ranking
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Identifying opinion leaders in the blogosphere
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Discovering information diffusion paths from blogosphere for online advertising
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Dynamic prediction of communication flow using social context
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Recommending topics for self-descriptions in online user profiles
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Mining social networks using heat diffusion processes for marketing candidates selection
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Modeling multi-step relevance propagation for expert finding
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User grouping behavior in online forums
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Temporal and information flow based event detection from social text streams
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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Learning influence probabilities in social networks
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Capturing implicit user influence in online social sharing
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FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
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ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Determining user expertise for improving recommendation performance
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PRemiSE: personalized news recommendation via implicit social experts
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Simulating the Diffusion of Information: An Agent-Based Modeling Approach
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Exploring friend's influence in cultures in Twitter
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Personalized influence maximization on social networks
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Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
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We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users' access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with respect to the categories. Once an item has been accessed by early adopters, personalized recommendations are achieved by estimating whom the information will be propagated to with high probabilities. In our experiments with an online document recommendation system, the results demonstrate that the EABIF and the TEABIF can respectively achieve an improved (precision, recall) of (91.0%, 87.1%) and (108.5%, 112.8%) compared to traditional collaborative filtering, given an early adopter exists.