Enhanced Architecture for Cold Start Product Recommendation Using Microblogging Information

Abstract

This paper presents an enhanced web application, using web services for interconnecting three various servers like, social network, E-commerce application and news channels. By Using Artificial Neural Network (ANN) and Text categorization the recommended products will be classified. Also enhanced microblogging information has been implemented for efficient client server process. In added with three tier architecture has been used. I propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. I propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we suggest learning both users’ and products’ feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation.