On visual similarity based interactive product recommendation for online shopping

With the rapid development of e-commerce and explosive growth of online shopping market, the problem of how to optimize the process of guiding the user to the huge amount of online products have been urgent. Existing recommender systems use information from users´ profiles (demographic filtering), similar neighbors (collaborative filtering), and textual description (content-based model) to make recommendations, which easily generate irrelevant suggestions to users due to the ignorance of users´ intentions and the visual similarity among products. In this paper, we proposed an interactive product recommendation method, which considers not only the product diversity but also the visual similarity, to interactively capture a user´s real intention and refine the product recommendation result based on the user´s real product interests. Our algorithm is experimentally evaluated under a real-world user log, and shown to significantly improve recommendation accuracy over the traditional approaches.