Application of Deep Learning Model Convolution Neural Network for Effective Web Information Retrieval

Abstract

Convolution neural network (CNN) is the most popular deep learning method that has been used for various applications like image recognition, computer vision, and natural language processing. In this chapter, application of CNN in web query session mining for effective information retrieval is explained. CNN has been used for document analysis to capture the rich contextual structure in a search query or document content. The document content represented in matrix form using Word2Vec is applied to CNN for convolution as well as maxpooling operations to generate the fixed length document feature vector. This fixed length document feature vector is input to fully connected neural network (FNN) and generates the semantic document vector. These semantic document vectors are clustered to group similar document for effective web information retrieval. An experiment was performed on the data set of web query sessions, and results confirm the effectiveness of CNN in web query session mining for effective information retrieval.