A Hybrid Multi-Feature Semantic Similarity Based Online Social Recommendation System Using CNN

Abstract

Modern organizations are keen to work towards their customer needs. To achieve this, analyzing their activities and identifying their interest in any entity becomes important. Every user has been identified as the most important factor in point of organization, and they never give up even a single user. Several approaches have been discussed earlier, which use artificial intelligence to mine the users and their interest in the problem. However, the deep learning algorithms are identified as most efficient in identifying the user interest but suffer to achieve higher performance. Towards this issue, an efficient multi-feature semantic similarity-based online social recommendation system has been proposed. The method uses Convolution Neural Network (CNN) to train and predict user interest in any topic. Each layer has been identified as a single interest, and neurons of the layers are initialized with huge data set. The neuron estimates the Multi-Feature Semantic Similarity (MFSS) towards each interest of the user. Finally, the method identifies the single interest for the user by ranking each interest to produce recommendations to the user. The proposed algorithm improves the performance of recommendation generation with less false ratio.