{"title":"将查询映射到问题:了解用户的信息需求","authors":"Yunjun Gao, Lu Chen, Rui Li, Gang Chen","doi":"10.1145/2484028.2484138","DOIUrl":null,"url":null,"abstract":"In this paper, for the first time, we study the problem of mapping keyword queries to questions on community-based question answering (CQA) sites. Mapping general web queries to questions enables search engines not only to discover explicit and specific information needs (questions) behind keywords queries, but also to find high quality information (answers) for answering keyword queries. In order to map queries to questions, we propose a ranking algorithm containing three steps: Candidate Question Selection, Candidate Question Ranking, and Candidate Question Grouping. Preliminary experimental results using 60 queries from search logs of a commercial engine show that the presented approach can efficiently find the questions which capture user's information needs explicitly.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Mapping queries to questions: towards understanding users' information needs\",\"authors\":\"Yunjun Gao, Lu Chen, Rui Li, Gang Chen\",\"doi\":\"10.1145/2484028.2484138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, for the first time, we study the problem of mapping keyword queries to questions on community-based question answering (CQA) sites. Mapping general web queries to questions enables search engines not only to discover explicit and specific information needs (questions) behind keywords queries, but also to find high quality information (answers) for answering keyword queries. In order to map queries to questions, we propose a ranking algorithm containing three steps: Candidate Question Selection, Candidate Question Ranking, and Candidate Question Grouping. Preliminary experimental results using 60 queries from search logs of a commercial engine show that the presented approach can efficiently find the questions which capture user's information needs explicitly.\",\"PeriodicalId\":178818,\"journal\":{\"name\":\"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2484028.2484138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2484028.2484138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mapping queries to questions: towards understanding users' information needs
In this paper, for the first time, we study the problem of mapping keyword queries to questions on community-based question answering (CQA) sites. Mapping general web queries to questions enables search engines not only to discover explicit and specific information needs (questions) behind keywords queries, but also to find high quality information (answers) for answering keyword queries. In order to map queries to questions, we propose a ranking algorithm containing three steps: Candidate Question Selection, Candidate Question Ranking, and Candidate Question Grouping. Preliminary experimental results using 60 queries from search logs of a commercial engine show that the presented approach can efficiently find the questions which capture user's information needs explicitly.