Improving user interest inference from social neighbors.
ABSTRACT 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.
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ABSTRACT: With the advancements of mobile phones and the integration of multiple communication interfaces, online social interaction between users is no longer restricted to a specific place with connectivity to the Internet but can happen anywhere and at any time. This has promoted the development of mobile social applications to enable opportunistic interactions with co-located users. One of the challenging problems in such interactions is to discover interaction opportunities with nearby users. Existing works focus on properties related to mobile users in order to find similar users in the surrounding area; these works depend on predefined logic such as conditional statements to recommend spontaneous social interaction opportunities. However, the social implications of the place in which the interaction is taking place are an important factor for recommendations, as those implications provide hints about the most plausible types of interactions among co-located users. In this work, we present a middleware called SpinRadar which is designed to support spontaneous interactions between co-located users by taking into account the semantics of a place, which we call ‘placeness.’ Our evaluation shows that the proposed scheme satisfies users much more than existing schemes.Personal and Ubiquitous Computing 02/2014; · 1.13 Impact Factor
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ABSTRACT: Different users have different needs, it is increasingly difficult to recommend interested topics to them. The micro-blogging system can expose user interests from individual behaviors along with his/her social connections. It also offers an opportunity to investigate how a large-scale social system recommends personal preferences according to the temporal, spatial and topical aspects of users activity. Here we focus on the problem of mining user interest and modeling its evolution on the micro-blogging system for recommendation. We learn the user preference on topics from the visited micro-bloggings as user interest using text mining techniques. We then extend this concept with user's social connection on different topics. Moreover, we study the evolution of the user interest model and finally recommend the most preferred micro-bloggings to a user. Experiments on a large scale of micro-blogging dataset shows that our model outperforms traditional approaches and achieves considerable performance on recommending interested posts to a user.Proceedings of the 14th international conference on Web-Age Information Management; 06/2013
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ABSTRACT: Uncovering user interest plays an important role to develop personalized systems in various fields including the Web and pervasive computing. In particular, online social networks (OSNs) are being spotlighted as the means to understand users' social behavior out of abundant online social information. In this paper, we explore a computational method of inferring user interest in Facebook by combining the degree of familiarity and topic similarity with social neighbors based on social correlation phenomenon. By conducting a question-naire survey, we demonstrate that our proposed method increases the accuracy of inference by 12.4% compared to existing methods which do not consider the latent topic structure implied in social contents.Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01; 12/2012