In recent years, the boundaries between e-commerce and convivial networking have become increasingly blurred. Many e-commerce websites support the mechanism of gregarious authenticate where users can sign on the websites utilizing their gregarious network identities such as their Face book or Twitter accounts. Users can withal post their incipiently purchased products on micro blogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site

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... rt product recommendation, which aims to recommend products from e-commerce websites to users at convivial networking sites in cold-start situations, a quandary which has infrequently been explored afore. A major challenge is how to leverage cognizance extracted from gregarious networking sites for cross-site cold-start product recommendation. We propose to use the linked users across gregarious networking sites and e-commerce websites (users who have gregarious networking accounts and have made purchases on e-commerce websites) as a bridge to map users' gregarious networking features to another feature representation for product recommendation. In categorical, we propose learning both users' and products' feature representations (called utilizer embedding and product embedding, respectively) from data accumulated from e-commerce websites utilizing recurrent neural networks and then apply a modified gradient boosting trees method to transform users' gregarious networking features into utilizer embedding. Related Work: In our recommendation system for recommending colleges, we decided to take a different approach to the problem. Existing approaches tend to focus on user-item matrix techniques and neighbourhood approach, and their models reflect this line of thinking. We still do similarity calculations, but in a different way for recommending colleges as venues. There are some concepts that we use, which are common to most currently existing recommendation colleges. our project systems rely on information derived from the online of users, such as opinions or ratings, to form predictions, or produce recommendation of colleges. Existing collaborative filtering techniques involve generating a user item in fake matrix, from which recommendation results could be derived.