290 research outputs found

    PAUSANIAS: Final activity report

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    Search engines, such as Google and Yahoo!, provide efficient retrieval and ranking of web pages based on queries consisting of a set of given keywords. Recent studies show that 20% of all Web queries also have location constraints, i.e., also refer to the location of a geotagged web page. An increasing number of applications support location-based keyword search, including Google Maps, Bing Maps, Yahoo! Local, and Yelp. Such applications depict points of interest on the map and combine their location with the keywords provided by the associated document(s). The posed queries consist of two conditions: a set of keywords and a spatial location. The goal is to find points of interest with these keywords close to the location. We refer to such a query as spatial-keyword query. Moreover, mobile devices nowadays are enhanced with built-in GPS receivers, which permits applications (such as search engines or yellow page services) to acquire the location of the user implicitly, and provide location-based services. For instance, Google Mobile App provides a simple search service for smartphones where the location of the user is automatically captured and employed to retrieve results relevant to her current location. As an example, a search for pizza results in a list of pizza restaurants nearby the user. In this research project, we studied how preference queries can be extended for supporting also keywords. To this end we first studied preference queries in order to establish techniques that can be extended for supporting keywords (Chapter 1). Moreover, we proposed Top-k Spatio-Textual Preference Queries and proposed a novel indexing scheme and two algorithms for supporting efficient query processing (Chapter 2). We also studied the problem of maximizing the influence of spatio-textual objects based on reverse top-k queries and keyword selection (Chapter 3). Finally, we analyze the properties of geotagged photos of Flickr, and propose novel location-aware tag recommendation methods (Chapter 4)

    Improving Marketing Intelligence Using Online User-Generated Contents

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