{"title":"Towards Usability on Reverse Top-k Geo-Social Keyword Query Results","authors":"Pengfei Jin","doi":"10.1109/MDM.2019.00-16","DOIUrl":null,"url":null,"abstract":"The prevalence of location-based social networks gives rise to the study of Geo-Social Keyword Query (GSKQ), where the Reverse Top-k Geo-Social Keyword Query (RkGSKQ) is a key technique used to detect prospective customers. Existing RkGSKQ solutions only focus on query efficiency, but ignore the quality of query results. When the query issuer obtained unexpected query results, no suggestion was offered to aid them get better ones. Thus, the overall utility of this query remains a problem. Towards this end, this paper considers the usability of RkGSKQ results and study two novel problems, i.e., maximizing the size of RkGSKQ results and why-not questions on RkGSKQ, both of which have potential applications in market analysis.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDM.2019.00-16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The prevalence of location-based social networks gives rise to the study of Geo-Social Keyword Query (GSKQ), where the Reverse Top-k Geo-Social Keyword Query (RkGSKQ) is a key technique used to detect prospective customers. Existing RkGSKQ solutions only focus on query efficiency, but ignore the quality of query results. When the query issuer obtained unexpected query results, no suggestion was offered to aid them get better ones. Thus, the overall utility of this query remains a problem. Towards this end, this paper considers the usability of RkGSKQ results and study two novel problems, i.e., maximizing the size of RkGSKQ results and why-not questions on RkGSKQ, both of which have potential applications in market analysis.