Unified Metasearch Based Retrival System - IEEE PHP Projects

[Kalyan]

Unified Metasearch Based Retrival System

The unified model which, given the ranked lists of documents returned by multiple retrieval systems in response to a given query, simultaneously solves the problems of

(1) fusing the ranked lists of documents in order to obtain a high-quality combined list (metasearch);

(2) generating document collections likely to contain large fractions of relevant documents (pooling); and

(3) accurately evaluating the underlying retrieval systems with small numbers of relevance judgments (efficient system assessment).

Our approach is based on the Hedge algorithm for on-line learning. In effect, our proposed system learns which documents are likely to be relevant from a sequence of on-line relevance judgments. In experiments using TREC data, our methodology is shown to outperform standard methods for meta search, pooling, and system evaluation, often remarkably so.

In the absence of relevance judgments, Hedge produces metasearch lists whose quality equals or exceeds that of benchmark techniques such as CombMNZ and Condorcet.

While in the presence of relevance judgments, the performance of Hedge increases rapidly and dramatically. When applied to the problems of pooling and system evaluation, Hedge identifies relevant documents very quickly, and these documents form an excellent and efficient pool for evaluating the quality of retrieval systems.