Survey Kernel Optimized Regression Model for Product Recommendation

The boundaries between e-commerce and social networks have become increasingly blurred. Many e-commerce websites support social login mechanisms, and users can use their social network identities (such as Facebook or Twitter accounts) to log in on websites. Users can also post new purchased products on Weibo and link to e-commerce product pages. It proposes a novel solution for cross-site cold start product recommendation, which aims at recommending e-commerce site products to social networking site users in the context of "cold start", which is a problem rarely discussed before. One of the major challenges is how to use cross-site cold start product recommendations using knowledge extracted from social networking sites. It proposes to use linked users as bridges across social networking sites and e-commerce sites (users who have social network accounts and who have already shopped on e-commerce sites), mapping the user's social networking capabilities to another functional representation of product recommendations. Specifically, it is recommended to learn the user's and product's characteristic representation (referred to as user embedding and product embedding, respectively) from data collected from e-commerce websites that use recursive neural networks, and then apply the modified gradient-enhanced tree method to change the user's social network. Feature embedded user. Then develop a feature-based matrix decomposition method that can use learning user embedding for cold start product recommendation [1]. Lija John | Vani V Prakash"Survey Kernel Optimized Regression Model for Product Recommendation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11306.pdf http://www.ijtsrd.com/computer-science/data-miining/11306/survey-kernel-optimized-regression-model-for-product-recommendation/lija-john