Application of convolution neural network in web query session mining for personalised web search

In this paper, a deep learning convolution neural network (CNN) is applied in web query session mining for effective personalised web search. The CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic clusters means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.