a b s t r a c tThere is currently a number of research work performed in the area of bridging the gap between Information Retrieval (IR) and Online Social Networks (OSN). This is mainly done by enhancing the IR process with information coming from social networks, a process called Social Information Retrieval (SIR). The main question one might ask is What would be the benefits of using social information (no matter whether it is content or structure) into the information retrieval process and how is this currently done?With the growing number of efforts towards the combination of IR and social networks, it is necessary to build a clearer picture of the domain and synthesize the efforts in a structured and meaningful way. This paper reviews different efforts in this domain. It intends to provide a clear understanding of the issues as well as a clear structure of the contributions. More precisely, we propose (i) to review some of the most important contributions in this domain to understand the principles of SIR, (ii) a taxonomy to categorize these contributions, and finally, (iii) an analysis of some of these contributions and tools with respect to several criteria, which we believe are crucial to design an effective SIR approach. This paper is expected to serve researchers and practitioners as a reference to help them structuring the domain, position themselves and, ultimately, help them to propose new contributions or improve existing ones.

We propose a new approach for social and personalized query expansion using social structures in the Web 2.0. While focusing on social tagging systems, the proposed approach considers (i) the semantic similarity between tags composing a query, (ii) a social proximity between the query and the user profile, and (iii) on the fly, a strategy for expanding user queries. The proposed approach has been evaluated using a large dataset crawled from del.icio.us.