Deep learning techniques for relevant product recommendation using social network data

Abstract

Recently, social influence has made adverse impact in product newlinemarketing areas. Moreover, this has been considered rarely as a conventional newlinerecommender system. The growth of trends in online shopping, blogs, social newlinenetworks influence the consumers to understand the best and quality product newlinefrom the posts, reviews and ratings of the users. A recommender system is an newlineessential part of online shopping or platforms such as Google, Facebook and newlineTwitter etc. This system is a subset of information filter method that can newlinepredict the ratings or preference which a customer offered to the particular newlinecommodity. Feedback, Opinion, and reviews are being expressed by the Users newlinethrough social networks. newlineThe recommender system is a subclass of information or data that newlinefilters out and predicts the ratings or preferences at which the user might offer newlinea specified item. The traditional approaches were having reduced amount of newlinesearch outcome quality with minimal accuracy rate. So as to overcome this newlineissue and with intention to augment prediction quality, this proposed approach newlineis presented using deep learning techniques. In the first stage work, a new newlineparadigm is presented for relevant product recommendation in social newlinenetworks. The main motive of this approach is to permit the computers to newlinelearn in an automatic manner devoid of any human interventions or guidance newlinethereby regulating the activities accordingly. newline