A Framework for Extracted Data from Social Networking Sites in Addressing the Cross-Site Cold-Start Product Recommendation - Alka Kumari; KDV Prasad; C. Sushama; Archana P. Kale; Revati M. Wahul; Shrddha Sagar (International Journal of Intelligent Systems and Applications in Engineering, 2022)
With rise of online social networks, social network-based proposal approach is prevalently utilized. Significant advantage of this method is capacity of managing the issues with cold-start clients. Notwithstanding social networks, client trust data likewise assumes a significant part to acquire dependable proposals. Deep learning (DL) has drawn in expanding consideration by virtue of its huge handling power in errands, like discourse, picture, or text handling. This research propose novel technique in social networking site data based product recommendation for cross-site cold-startusing deep learning techniques. Here the input data has been collected as social networking data with addressing of cross-site cold-startproducts. The input data has been processed for noise removal, smoothening and normalization. The processed data features has been extracted using deep convolutional capsulenet neural network. The experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, MAP, RMSE. We perform broad trials on certifiable informal community information to exhibit the precision and viability of our proposed approach in correlation with other cutting edge strategies .The proposed technique attained accuracy of 93%, precision of 91%, recall of 85%, F-1 score of 80%, MAP of 52%, RMSE of 55%.