GeoSocial Topk and Skyline Keyword Queries on Road Networks The rapid growth of GPSenabled mobile devices has popularized many locationbased applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most realworld applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geosocial topk keyword queries and geosocial skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

Improving BanditBased Recommendations with Spatial Context Reasoning: An Online Evaluation The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a citywide free public WiFi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by banditbased recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping WiFi access points according to their usersu0027 visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves banditbased recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geocontext, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the banditbased recommendation system, both in terms of accuracy and user participation.