GroupFinder: a new approach to top-k point-of-interest group retrieval

The notion of point-of-interest (PoI) has existed since paper road maps began to include markings of useful places such as gas stations, hotels, and tourist attractions. With the introduction of geopositioned mobile devices such as smartphones and mapping services such as Google Maps, the retrieval of PoIs relevant to a user's intent has became a problem of automated spatio-textual information retrieval. Over the last several years, substantial research has gone into the invention of functionality and efficient implementations for retrieving nearby PoIs. However, with a couple of exceptions existing proposals retrieve results at single-PoI granularity. We assume that a mobile device user issues queries consisting of keywords and an automatically supplied geo-position, and we target the common case where the user wishes to find nearby groups of PoIs that are relevant to the keywords. Such groups are relevant to users who wish to conveniently explore several options before making a decision such as to purchase a specific product. Specifically, we demonstrate a practical proposal for finding top-k PoI groups in response to a query. We show how problem parameter settings can be mapped to options that are meaningful to users. Further, although this kind of functionality is prone to combinatorial explosion, we will demonstrate that the functionality can be supported efficiently in practical settings.