Personalization and context-awareness in social local search: State-of-the-art and future research challenges

Location-based services (LBS) are now the platforms for aggregating relevant information about users and understanding their mobile behavior and preferences based on the location histories. The increasing availability of large amounts of spatio-temporal data brings us opportunities and challenges to automatically discover valuable knowledge. While context-aware properties quickly became the key of the success of these pervasive applications, information related to user preferences and social signals still lack of adequate capitalization. Local search in LBSs is a peculiar service where recent and current interests, the network of explicit and implicit social interactions between users can be combined for effectively performing fine-tuned and personalized recommendations of points of interest. In this article we present the various and peculiar aspects of local search in mobile scenarios. Then we explore the added value of personalization and the benefits of considering social signals, summarizing open challenges and emerging technologies.