Just-In-Time Recommender Systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. In this paper, we propose a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for users to initiate any interaction.

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With the rapid growth of mobile applications, the user is increasingly confronted with a lot of information and tend to reject notifications sent by applications installed within his/her mobile device. This rejection affects the performance of many systems, especially proactive recommender systems. Therefore, it is no longer enough for a recommender system to determine what to recommend according to users' needs, but it also has to deal with the risk of disturbing the user during the recommendation process. We believe that the several embedded applications within the user's device along with other parameters could help understand and assess the user's interruptibility in some situations. In this paper, we address intrusiveness within a proactive recommendation approach that makes use of the user's context and the applications embedded within the user's mobile device in order to assess the intrusiveness level of a given situation before recommending.

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The overwhelming advances in mobile technologies allow recommender systems to be highly contextualized and able to deliver recommendation without an explicit request. However, it is no longer enough for a recommender system to determine what to recommend according to the users' needs, but it also has to deal with the risk of disturbing the user during recommendation. We believe that mobile technologies along with contextual information may help alleviate this issue. In this paper, we address intrusiveness as a probabilistic approach that makes use of the several embedded applications within the user's device and the user's contextual information in order to gure out intrusive recommendations that are subject to rejection. The experiments that we conducted have shown that the proposed approach yields promising results.

The Proactive Context Aware Recommender Systems aim at combining a set of technologies and knowledge about the user context not only in order to deliver the most appropriate information to the user need at the right time but also to recommend it without a user query. In this paper, we propose a contextualized proactive multi-domain recommendation approach for mobile devices. Its objective is to efficiently recommend relevant items that match users' personal interests at the right time without waiting for users to initiate any interaction. Our contribution is divided into two main areas: The modeling of a situational user profile and the definition of an aggregation frame for contextual dimensions combination.

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