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

Abstract

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.

Keywords: Big data; Deep recurrent; Gaussian Nesterov’s; Gradient; Predictive; Recommendation; Social network.

Fig. 1

Block diagram of DR-GNOG

Fig. 2

Structure of Confidence-aware Tweet Rank Social Network model

Fig. 3

Sample user-tweet ranking matrix

Fig. 4

Recommendation accuracy of three recommendation method [changes made] using sentiment 140 dataset

Fig. 5

Recommendation accuracy of three recommendation method [changes made] using coronavirus tweets NLP text classification dataset

Fig. 6

Recommendation time of three recommendation method [changes made] using sentiment 140 dataset

Fig. 7

Recommendation time of three recommendation method [changes made] using coronavirus tweets NLP text classification dataset

Fig. 8

Recall rate of three recommendation method [changes made] using sentiment140 dataset

Fig. 9

Recall rate of three recommendation method [changes made] using coronavirus tweets NLP text classification dataset

Fig. 10

Precision of three recommendation method [changes made] using sentiment 140 dataset

Fig. 11

Precision of three recommendation method [changes made] using coronavirus tweets NLP text classification dataset