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Why collective inference improves relational classification
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Tight approximation algorithms for maximum general assignment problems
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SmallBlue: People Mining for Expertise Search
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Influence and correlation in social networks
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Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels
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Inferring private information using social network data
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Connections between the lines: augmenting social networks with text
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You are who you know: inferring user profiles in online social networks
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On the quality of inferring interests from social neighbors
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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
Mining user interest and its evolution for recommendation on the micro-blogging system
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SpinRadar: a spontaneous service provision middleware for place-aware social interactions
Personal and Ubiquitous Computing
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Prior research has provided some evidence of social correlation (i.e., "you are who you know"), which makes it possible to infer one's interests from his or her social neighbors. However, it is also shown to be challenging to consistently obtain high quality inference. This challenge can be partially attributed to the fact that people usually maintain diverse social relationships, in order to tap into diverse information and knowledge. It is unlikely that a person would possess all interests of his/her social neighbors. Instead, s/he may selectively acquire just a subset of them. This paper intends to improve inferring interests from neighbors given this observation. We conduct this study by implementing a privacy-preserving large distributed social sensor system in a large global IT company to capture the multifaceted activities (e.g., emails, instant messaging, social bookmarking, etc.) of 25K+ people. These activities occupy the majority of employees' time, and thus, provide a higher quality view of the diverse aspects of their professional interests compared to the friending activity on online social networking sites. In this paper, we propose a technique that exploits the correlation among the attributes that a person possesses to improve social-correlation-based inference quality. Our technique offers two unique contributions. First, we demonstrate that the proposed technique can significantly improve inference quality by as much as 76.1%. Second, we study the interaction between the two factors: social correlation and attribute correlation under different situations. The results can inform practical applications how the inference quality would change in various scenarios.