Deep recurrent Gaussian Nesterovs recommendation using multi-agent in social networks

Due to increasing volume of big data the high volume of information in Social Network put a stop to users from acquiring serviceable information intelligently so many recommendation systems have emerged. Multi-agent Deep Learning gains rapid attraction, and the latest accomplishments address problems with real-world complexity. With big data precise recommendation has yet to be answered. In proposed work Deep Recurrent Gaussian Nesterov's Optimal Gradient (DR-GNOG) that combines deep learning with a multi-agent scenario for optimal and precise recommendation. The DR-GNOG is split into three layers, an input layer, two hidden layers and an output layer. The tweets obtained from the users are provided to the input layer by the Tweet Accumulator Agent. Then, in the first hidden layer, Tweet Classifier Agent performs optimized and relevant tweet classification by means of Gaussian Nesterov's Optimal Gradient model. In the second layer, a Deep Recurrent Predictive Recommendation model is designed to concentrate on the vanishing gradient issue arising due to updated tweets obtained from same user at different time instance. Finally, with the aid of hyperbolic activation function in the output layer, building block of the predictive recommendation is obtained. In the experimental study the proposed method is found better than existing GANCF and Bootstrapping method 13-21% in case of recommendation accuracy, 22-32% better in recommendation time and 15-22% better in recall rate.