{"title":"使用递归伪相关反馈策略改进类似文档检索","authors":"Kyle Williams, C. Lee Giles","doi":"10.1145/2910896.2925468","DOIUrl":null,"url":null,"abstract":"We present a recursive pseudo relevance feedback strategy for improving retrieval performance in similarity search. The strategy recursively searches on search results returned for a given query and produces a tree that is used for ranking. Experiments on the Reuters 21578 and WebKB datasets show how the strategy leads to a significant improvement in similarity search performance.","PeriodicalId":109613,"journal":{"name":"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","volume":"61 11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Improving similar document retrieval using a recursive pseudo relevance feedback strategy\",\"authors\":\"Kyle Williams, C. Lee Giles\",\"doi\":\"10.1145/2910896.2925468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a recursive pseudo relevance feedback strategy for improving retrieval performance in similarity search. The strategy recursively searches on search results returned for a given query and produces a tree that is used for ranking. Experiments on the Reuters 21578 and WebKB datasets show how the strategy leads to a significant improvement in similarity search performance.\",\"PeriodicalId\":109613,\"journal\":{\"name\":\"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)\",\"volume\":\"61 11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2910896.2925468\",\"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 IEEE/ACM Joint Conference on Digital Libraries (JCDL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2910896.2925468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving similar document retrieval using a recursive pseudo relevance feedback strategy
We present a recursive pseudo relevance feedback strategy for improving retrieval performance in similarity search. The strategy recursively searches on search results returned for a given query and produces a tree that is used for ranking. Experiments on the Reuters 21578 and WebKB datasets show how the strategy leads to a significant improvement in similarity search performance.