Using collaborative filtering to weave an information tapestry
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Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Dogear: Social bookmarking in the enterprise
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Improving web search ranking by incorporating user behavior information
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The structure of information pathways in a social communication network
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Social influence analysis in large-scale networks
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Predicting user interests from contextual information
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ExpertiseNet: relational and evolutionary expert modeling
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Visual analysis of implicit social networks for suspicious behavior detection
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Improving user interest inference from social neighbors
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Large-scale behavioral targeting with a social twist
Proceedings of the 20th ACM international conference on Information and knowledge management
Who resemble you better, your friends or co-visited users
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Putting humans in the loop: Social computing for Water Resources Management
Environmental Modelling & Software
Modeling user posting behavior on social media
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Trust prediction via aggregating heterogeneous social networks
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Inferring User Interest Using Familiarity and Topic Similarity with Social Neighbors in Facebook
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Speeding up large-scale learning with a social prior
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Maximizing acceptance probability for active friending in online social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Social trust prediction using rank-k matrix recovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Social trust prediction using heterogeneous networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
SpinRadar: a spontaneous service provision middleware for place-aware social interactions
Personal and Ubiquitous Computing
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This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his/her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8%, and inference quality of explicit interests by up to 101%.