Razvan Stefan Bot
ABSTRACT
This paper presents a document representation improvement technique named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called support this algorithm improves document representations and generates higher quality indexes. At the same time, the algorithm is efficient and scalable, suited for retrieval systems managing large document collections. The results of the preliminary evaluation reveal that the RFA algorithm is able to reduce the index dimensionality while improving retrieval effectiveness.
- Improving document representation by accumulating relevance feedback (abstract only): the relevance feedback accumulation algorithm
Recommendations
Improving document representations using relevance feedback: the RFA algorithm
CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge managementIn this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function ...
A Clustering Approach to Improving Pseudo-Relevance Feedback: Improving Retrieval Effetiveness by Removing Noisy Documents
ISISE '12: Proceedings of the 2012 Fourth International Symposium on Information Science and EngineeringPseudo relevance feedback is an effective technique for improving retrieval results, which assumes a small number of top-ranked documents in the initial retrieval results are relevant and selects from these documents related terms to the query to ...
Comments