{"title":"基于用户反馈语义的桌面搜索分析及结果排序","authors":"M. S. Raje","doi":"10.1109/SITIS.2016.46","DOIUrl":null,"url":null,"abstract":"This paper explores the ranking of search results in a desktop search engine. A comparison between web search and desktop search and the respective user behaviour during them, is made. This is followed by an analysis of the existing desktop search tools. The paper then discusses a Bayesian technique to collect user feedback and use it to rank the results of existing search frameworks. The results of evaluation of 449 query-result pairs are reported. It is shown that incorporating user feedback improves the ranking of results by as much as 7.32%.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Analysis of Desktop Search and Ranking of Their Results Based on Semantics from User Feedback\",\"authors\":\"M. S. Raje\",\"doi\":\"10.1109/SITIS.2016.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores the ranking of search results in a desktop search engine. A comparison between web search and desktop search and the respective user behaviour during them, is made. This is followed by an analysis of the existing desktop search tools. The paper then discusses a Bayesian technique to collect user feedback and use it to rank the results of existing search frameworks. The results of evaluation of 449 query-result pairs are reported. It is shown that incorporating user feedback improves the ranking of results by as much as 7.32%.\",\"PeriodicalId\":403704,\"journal\":{\"name\":\"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2016.46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2016.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of Desktop Search and Ranking of Their Results Based on Semantics from User Feedback
This paper explores the ranking of search results in a desktop search engine. A comparison between web search and desktop search and the respective user behaviour during them, is made. This is followed by an analysis of the existing desktop search tools. The paper then discusses a Bayesian technique to collect user feedback and use it to rank the results of existing search frameworks. The results of evaluation of 449 query-result pairs are reported. It is shown that incorporating user feedback improves the ranking of results by as much as 7.32%.
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