User feedback based metasearching using neural network
Metasearch engines are the web services that receive user queries and dispatch them to multiple crawler based search engines. After this, they collect the returned search results, reorder them and present the reordered list to the end user. To combine the results from different search engines, a metasearch engine may use different rank aggregation techniques to aggregate the various rankings of the search results to generate an overall ranking. If different rank aggregation techniques are used to collate search results, the results of metasearching for the same query may vary for the same set of participating search engines. In this paper, we discuss a metasearching technique that utilizes neural network based rank aggregation. Here, we formulate the rank aggregation problem as a function approximation problem. As the multilayer perceptrons are considered universal approximators, we use a multilayer perceptron for rank aggregation. We compare the performance of the neural network based method with four other methods namely rough set based method, modified rough set based method, Bordas method and a Markov Chain based method (MC2) using three independent evaluators. Experimentally, we find that the neural network based method performs better than each of these four methods.
user feedback metasearching neural network correlation coefficient performance evaluation