Abstract: This Decade, the boundaries between e-commerce and social networking have become increasingly blurred. Lots of e-commerce web Application support the process of social login where users can sign on the websites using their social network username and password authentication such as their Twitter or Facebook accounts. Social Network users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation. We aim to recommend e-commerce product from e-commerce websites to users at social networking websites in “cold-start” situations. Cold-start situation is a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we propose learning both users’ and products’ feature representations from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the microblogging service FACEBOOK and the largest e-commerce website AMAZON have shown the effectiveness of our proposed framework.
Keywords: Cold start, Product Recommendation, E-commerce, Micro-blogs, Product Demography, Data mining, Information Search.