Web User Query Optimization by Machine Learning Source code with Dataset

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With the drastic increase of the digital text data on the servers, it is necessary to develop new algorithms by the researchers. Considering this fact work has focus on one of the issue of the keyword identification which is build by the different documents. The most important role of the information retrieval is to satisfy user. Query recommendation is one of the best methods for assisting users to fulfill the user’s information need by suggesting queries related to current users need by maintaining query log processing files. With the proper information of the ontology and the web usage of the web, feature vectors are developed for training the Error back propagation neural network. By the use of Error Back Propagation Neural Network classification technique the queries are handled in effective manner and consumes less time. This work improves the accuracy of the classification so the web server response time will become lesser. Precision and recall values are also good from segmentation point of view. In future, the different genetic approach for segmentation of user query can also be adopted.

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