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Recommender systems have been widely applied to e-commerce to help customers find products to purchase. Cold-start is characterized by the incapability of recommending due to the lack of enough ratings. In fact, solutions for the cold-start problem have been proposed for different contexts, but the problem is still unsolved. This paper presents a RS for new e-commerce users by using only their interactions in social networks to understand their preferences. The proposed Recommender System (RS) applies a content-based approach and improves the experience of new users by recommending specific products in a preferred identified user category by analysing their data from the social network. Therefore, it combines three social network elements: (1) direct user posts (e.g.: "tweets" from Twitter and "posts" from Facebook), (2) content "likes" (e.g.: option "like" on a "post" or "tweet" posted by another user), and (3) page "likes" (e.g.: option "like" on a Facebook page). The proposed RS was tested for a retail e-commerce, which usually not only has a large range of categories of products, but also has products within these categories. The difficulty in predicting a product increases sharply with a greater number of categories and products. According to the experiment conducted, the proposed RS demonstrated to be a reasonable alternative to cold-start, i.e., for users accessing e-commerce for the very first time.

Content-based Recommender System using Social Networks for Cold-start Users