A HYBRID SOCIAL SEARCH MODEL BASED ON THE USER'S ONLINE SOCIAL NETWORKS

Online social networks (OSNs) have become a new networking platform for connecting people through a variety of mutual relationships. Owing to its widespread use of applications such as email, online shopping, online business networking and instant messaging, OSNs play an indispensable part in people's work and life. People tend to seek information through their OSNs rather than the traditional search engine. This paper presents a hybrid social search model, which harnesses the user's social relation to generate the satisfying results. Upon receiving a user's query, the social search model aims to return a list of ranked answerers who might give the correct answers to that query. Two novel algorithms are proposed to calculate the result ranking: 1) Topic Relevance Rank (TRR) evaluates user's professional score on the relevant topics; 2) Social Relation Rank (SRR) captures the social relation strength between users. In addition, a topic classification label is defined to control the weight of these two algorithms. We describe the architecture of the social search model and give details of the ranking algorithms. A use-case is described to illustrate the potential applications of the proposed model. 3G RenRen Network, mobile edition of the biggest student social networking site in China is studied and the experiment results show that the two ranking algorithms under control of topic classification label can benefit the social search model evidently.