Grouping Users for Quick Recommendations of Text Documents Based on Deep Neural Network

The use of Recommendation Systems in any domain plays a vital role in almost all information technology applications. The major focus of this research paper deals with users more preferably e-learners using the proposed Recommendation system. The major objective in developing any Recommendation system is based on many factors like accuracy, preciseness and fast measures. Recommendations given to each user is based on his/her domain interest was time consuming in the past. This research paper deals with the development of a recommendation system which is based on accuracy and fastness measures. One of the factors for developing a fast recommendation system can be obtained by developing efficient algorithms for grouping the existing and the new users quickly so that further domain recommendations might be easier. The proposed framework is based on deep neural network, which proved to be an efficient algorithm for high dimensional data training and testing. The accuracy of the algorithm is justified by the generation of semantic hash codes generated from the users’ profile information and the subsequent hamming distance computation. The fast and the accuracy measures of the framework is justified and the experimental results are promising.