The explosion of the information available on the Internet has made traditional information retrieval systems; characterized by ʺone size fits allʺ approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user’s search background and environment, in order to improve the retrieval accuracy. In this chapter, we focus on mobile context, we highlight challenges they present for IR, and give an overview of CIR approaches applied in this environment. Then, we present our approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. Our experimental evaluation undertaken in front of Yahoo search shows that our approach improves the quality of top search results list and enhances search results precision. INTRODUCTION Information retrieval (IR) deals with the representation, storage, and access to information according to the user’s information need. The main goal of an information retrieval system (IRS) is to bring relevant documents to users in response to their queries. However, the explosion of the information available on the Internet and its heterogeneity has made traditional IRS less effective (Dervin & Nilan, 1986; Shamber, 1994). In (Budzik & Hammond, 2000) the authors show that the main reason is that traditional IRS do not take into account the user context in the retrieval process. Indeed, traditional retrieval models and system design are based solely on the query and the document collection which leads to providing the same set of results for different users when the same query is submitted. In order to tackle this problem, a key challenge in IR is: how to capture and how to integrate contextual information in the retrieval process in order to increase the search performance? In (Allan, 2002) contextual retrieval is defined as "combine search technologies and knowledge about query and user context into a single framework in order to provide the most appropriate answer for users information needs". Thus, contextual IR aims at optimizing the retrieval accuracy by involving two related steps: appropriately defining the context of user information needs, commonly called “search context”, and then adapting the search by taking it into account in the information selection process. One of the fundamental research questions in contextual IR is: which context dimensions should be considered in the retrieval process? Several studies proposed a specification of context within and across application domains (Göker & Myrhaug, 2002; Vieira, et al., 2007). Figure1, presents a context taxonomy presented in (Tamine, Boughanem & Daoud, 2009), it synthesizes five context specific dimensions listed below, that have been explored in contextual IR literature.