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Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-Social Keyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects
collected keywords of haptic related researches from several journals and used those as an input for conducting network analysis. Then, we also classified the areas of haptic researches by analyzing the relationship among keywords. From the network analysis, we found that haptic related researches could be classified into two
In online social networks (OSNs), user connections can be represented as a network. The network formed has distinct properties that distinguish it from other network topologies. In this work, we consider an unstructured keyword based social network topology where each edge has a trust value associated with it to
The boom of the spatial web has enabled spatial keyword queries that take a user location and multiple search keywords as arguments and return the objects that are spatially and textually relevant to these arguments. Recently, utilizing social data to improve search results, normally by giving a higher rank to the
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