Publication

Abstract : Deep learning is the most active research topic amongst data scientists and analysts these days. It is because deep learning has provided very high accuracy in various domains such as speech recognition, image processing and natural language processing. Researchers are actively working to deploy deep learning on information retrieval. Due to large-scale data generated by social media and sensor networks, it is quite difficult to train unstructured and highly complex data. Recommender system is intelligent information filtering technique which assists the user to find topic of interest within complex overloaded information. In this paper, our motive is to improve recommendation accuracy for large-scale heterogeneous complex data by integrating deep learning architecture. In our proposed approach ratings, direct and indirect trust values are fed in neural network using shared layer in autoencoder. Comprehensive experiment analysis on three public datasets proves that RMSE and MAE are improved significantly by using our proposed approach.