Date of Publication :9th June 2017

Abstract: Nowadays, it is possible to access many E-commerce websites by using users social network accounts like facebook, twitter etc. Users of social networks can able to post their newly purchased products in the microblogs, and can give links to the E-commerce web pages from where they are purchased. This paper presents a solution for cross-site cold start product recommendation. A major challenge for this problem is how to use the information taken from social networking sites for cold start product recommendation. This paper proposes, by using neural networks extract user features or user embeddings and product feature or product embeddings from the data collected from E-commerce websites. Then by using gradient boosting tree method on the social networking sites, collect user features and combine this with the user embeddings. Then by using matrix factorization method use these user embeddings for the cold start product recommendation.

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