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