Involving Common Media to Export Product Recommendation Using Existing Data

Abstract

There is an increasingly blurred line between e-commersce and social networking. Many e-commerce platforms support the social authentication process through which users can sign in using their social network identity, for example, on Facebook or Twitter. In addition, users can also post new items on microblogs with links to the website of the product for e-commerce. The purpose of this paper is to recommend goods for e-commerce web pages to users on social networking sites under "cold-start," an issue that was scarcely investigated before, in an innovative approach to the cold-start product advice. One of the main challenges for the advice is how to use the information derived from social networking platforms. We suggest using connected users through social networking websites and e-commerce websites as a bridge to map the functionality of social networking users to another feature for product suggestion and for social networking. In particular, we suggest learning the user and product characteristics of data obtained from e-commerce sites using recurring neural networks (known as the user embedding and the goods embedding), and then implement a revamped system of gradients boosting trees to turn user social networking features into user embedding