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Retrieval and feedback models for blog feed search
Abstract Blog feed search poses different and interBlog feed search poses different and interesting challenges from traditional ad hoc document retrieval. The units of retrieval, the blogs, are collections of documents, the blog posts. In this work we adapt a state-of-the-art federated search model to the feed retrieval task, showing a significant improvement over algorithms based on the best performing submissions in the TREC 2007 Blog Distillation task [12]. We also show that typical query expansion techniques such as pseudo-relevance feedback using the blog corpus do not provide any significant performance improvement and in many cases dramatically hurt performance. We perform an in-depth analysis of the behavior of pseudorelevance feedback for this task and develop a novel query expansion technique using the link structure in Wikipedia. This query expansion technique provides significant and consistent performance improvements for this task, yielding a 22% and 14% improvement in MAP over the unexpanded query for our baseline and federated algorithms respectively.ine and federated algorithms respectively.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Comments "our novel Wikipedia link-based approach obtained a greater than 13% improvement over no expansion (across large and small document models) in terms of both MAP and P@10" p. 354
Conclusion we presented an in-depth analysis of querywe presented an in-depth analysis of query expansion for blog feed retrieval. On this task, our novel Wikipedia link-based approach obtained a greater than 13% improvement over no expansion (across large and small document models) in terms of both MAP and P@10. Although this method did not generalize to the Terabyte Track ad hoc queries it does show promise for queries that represent more general information needs, similar to those typical of feed retrieval.imilar to those typical of feed retrieval.
Conference location Singapore, Singapore +
Data source Experiment responses  + , Wikipedia pages  +
Dates 20-24 +
Doi 10.1145/1390334.1390394 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Retrieval%2Band%2Bfeedback%2Bmodels%2Bfor%2Bblog%2Bfeed%2Bsearch%22  +
Has author Jonathan L. Elsas + , Jaime Arguello + , Jamie Callan + , Jaime G. Carbonell +
Has domain Computer science +
Has topic Query processing +
Month July  +
Pages 347-354  +
Peer reviewed Yes  +
Publication type Conference paper  +
Published in SIGIR '08 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval +
Publisher Association for Computing Machinery +
Research design Experiment  +
Research questions In this work we adapt a state-of-the-art federated search model to the feed retrieval task, showing a significant improvement over algorithms based on the best performing submissions in the TREC 2007 Blog Distillation task[12].
Revid 10,930  +
Theories Undetermined
Theory type Design and action  +
Title Retrieval and feedback models for blog feed search
Unit of analysis Article  +
Url http://dl.acm.org/citation.cfm?id=1390394  +
Wikipedia coverage Main topic  +
Wikipedia data extraction Dump  +
Wikipedia language English  +
Wikipedia page type Article  +
Year 2008  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:30:05  +
Categories Query processing  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:30:55  +
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