Using deep learning to improve recommendation with direct and indirect social trust

Abstract

Deep learning is advancement in machine learning with many hidden layers. It can provide better results even with less number of features. In deep learning, large scale data automated learning is possible with less feature engineering. It is of high significance for big data because of large scale of data available for training. Many applications areas such as image processing, speech recognition and social network analysis have proven to provide better results with the use of deep learning. Large scale of data is generated due to social networking sites, sensor networks and business transactions. It is very difficult for user to select any product or topic of interest, Social recommendation is active research area due to its significance in reducing information overload. It is intelligent systems which are used to provide suggestions to users for any product or topic. In this article, deep learning is applied on social recommendation technique with our proposed approach based on transitive trust. Cold start and sparsity are main limitations in social recommendation. Experiment analysis proves that these issues are resolved by using our proposed approach incorporated with deep learning.

Keywords:

• Deep learning
• Big data
• Social Network
• Social recommendation
• Social trust
• Feedforward model