Answering Non-Answer Questions on Reverse Top-k Geo-Social Keyword Queries

Due to the wide-spread use of geo-positioning technologies and geo-social networks, the reversetop-k geo-socialkeyword query has attracted considerable attention from both industry and research communities. A reverse top-k geo- social keyword(RkGSK) query findsthe users who are spatially near, textually similar,and socially relevantto a specified point of interest. RkGSK queriesare useful in many real-life applications. For example,they can helpthe query issuer identify potential customers in marketing decisions. However, thequery constraints couldbe too strict sometimes, making it hard to find any result for the RkGSK query. The query issuersmay wonder how to modify their originalqueries to get a certainnumber of query results. In this paper,we study non-answer questionson reverse top-k geo-social keywordqueries (NARGSK). Given an RkGSK queryand the requirednumber M of queryresults, NARGSK aim to find the refined RkGSK queryhaving M users in its resultset. To efficiently answerNARGSK, we propose two algorithms (ERQ and NRG) based on query relaxation. As this is the firstwork to addressNARGSK to the best of our knowledge, ERQ is the baselineextended from the state-of-the-art method,while NRG furtherimproves the efficiency of ERQ. Extensive experiments usingreal-life datasets demonstrate the efficiency of our proposedalgorithms, and theperformance of NRGis improved by a factorof 1–2 on average compared with ERQ.