Mining user-contributed photos for personalized product recommendation

With the advent and popularity of social media, users are willing to share their experiences by photos, reviews, blogs, and so on. The social media contents shared by these users reveal potential shopping needs. Product recommender is not limited to just e-commerce sites, it can also be expanded to social media sites. In this paper, we propose a novel hierarchical user interest mining (Huim) approach for personalized products recommendation. The input of our approach consists of user-contributed photos and user generated content (UGC), which include user-annotated photo tags and the comments from others in a social site. The proposed approach consists of four steps. First, we make full use of the visual information and UGC of its photos to mine user's interest. Second, we represent user interest by a topic distribution vector, and apply our proposed Huim to enhance interest-related topics. Third, we also represent each product by a topic distribution vector. Then, we measure the relevance of user and product in the topic space and determine the rank of each product for the user. We conduct a series of experiments on Flickr users and the products from Bing Shopping. Experimental results show the effectiveness of the proposed approach.