Comparison of Learning Performance and Retrieval Performance for Support Vector Machines Based Relevance Feedback Document Retrieval

2007 ◽  
Author(s):  
Takashi Onoda ◽  
Hiroshi Murata ◽  
Seiji Yamada
Keyword(s):  
Support Vector Machines ◽  
Relevance Feedback ◽  
Document Retrieval ◽  
Learning Performance ◽  
Support Vector ◽  
Retrieval Performance ◽  
Vector Machines

Improving Relevance Feedback via Using Support Vector Machines

2011 ◽  
Vol 255-260 ◽  
pp. 2028-2032
Author(s):  
Zi Long Chen ◽  
Yang Lu
Keyword(s):  
Support Vector Machines ◽  
Relevance Feedback ◽  
Support Vector ◽  
Retrieval Performance ◽  
New Approach ◽  
Retrieval Effectiveness ◽  
Vector Machines ◽  
Traditional Approaches ◽  
Feedback Techniques

Traditional relevance feedback technique could help improve retrieval performance. It usually utilize the most frequent terms in the relevant documents to enrich the user’s initial query. We re-examine this method and find that many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. This paper introduces a Support Vector Machines Based method to improve the retrieval results. Firstly, the classifier is trained on the feedback documents. Then, we can utilize this classifier to classify the rest of the documents and move relevant documents to the front of irrelevant documents. This new approach avoids modifying the initial query, so it’s a new direction for the relevance feedback techniques. Our Experiments on TREC dataset demonstrate that retrieval effectiveness can be improved more than 24.37% when our proposed approach is used.

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