Leveraging Social Network Data to Alleviate Cold-Start Problem in Recommender Systems

Abstract

Now-a-days, the gap between e-commerce and social networking became progressively diminished. Several e-commerce websites and mobile applications support the mechanism of social login wherever users will sign in the websites using their social network identities like their Facebook or Twitter accounts.Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper,we represent 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 noteworthy issue is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. In particular, we proposed the solution for cold-start recommendation by linking the users to social networking sites and ecommercewebsites i.e. customers who have social network identities and have purchased on e-commerce websites as a bridge to map users social networking features into another feature representation which can be easier for a product recommendation. Here we propose to learn by using recurrent neural networks both user’s and product’s feature representations called user embedding and product embedding from the data collected from e-commerce website and then apply a modified gradient boosting trees method to transform user’s social networking features into

user embeddings. Once obtained, then we will develop a feature-based matrix factorization approach which will use the user and product features for the cold-start product recommendation. Experimental results show that our approach effectively works and gives the best-recommended results in cold start situations.