Relevance feedback versus web search document clustering

The performance of an IR system is deteriorated by factors including short and vague queries put up by the users, ever increasing volume of documents on the web, users not knowing their exact information need etc. Relevance feedback (RF) and web search document clustering are techniques to improve the performance of an Information Retrieval (IR) system. Relevance feedback provides a method to get more relevant search result from an IR system using documents that are marked relevant by the user as a feedback to reformulate query. This refined query is then used to retrieve the documents. In document clustering approach, the search result is divided into thematic groups where documents of one group are similar to each other and dissimilar to the documents of other groups. This paper presents a report on the effectiveness of relevance feedback technique as compared to document clustering in context of web information retrieval and why document clustering is the most preferred approach.

[1]  Amanda Spink,et al.  Use of query reformulation and relevance feedback by Excite users , 2000, Internet Res..

[2]  Dong Zhou,et al.  Collaborative pseudo-relevance feedback , 2013, Expert Syst. Appl..

[3]  Giansalvatore Mecca,et al.  A new algorithm for clustering search results , 2007, Data Knowl. Eng..

[4]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[5]  Stephen E. Robertson,et al.  Selecting good expansion terms for pseudo-relevance feedback , 2008, SIGIR '08.

[6]  Oren Etzioni,et al.  Web document clustering: a feasibility demonstration , 1998, SIGIR '98.

[7]  Jing Zhang,et al.  Why Web-Based Pseudo Relevance Feedback Systems Fail , 2012, 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems.

[8]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[9]  Dawid Weiss,et al.  A survey of Web clustering engines , 2009, CSUR.

[10]  Mounia Lalmas,et al.  A survey on the use of relevance feedback for information access systems , 2003, The Knowledge Engineering Review.

[11]  Dawid Weiss,et al.  A concept-driven algorithm for clustering search results , 2005, IEEE Intelligent Systems.

[12]  Emanuele Della Valle,et al.  An Introduction to Information Retrieval , 2013 .

[13]  Mansaf Alam,et al.  A Review on Clustering of Web Search Result , 2012, ACITY.

[14]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[15]  Hongfei Lin,et al.  A Multiple Relevance Feedback Strategy with Positive and Negative Models , 2014, PloS one.

[16]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..