Consolidation: Metric+Active Learning and Its Applications for Cross-Domain Recommendation

How to discover potential users of new released products is an important task in marketing campaigns, especially for companies have many established users. Indeed, transfer learning is a natural choice for addressing such problem, which can leverage the user data from historical products (i.e., auxiliary domains) for recommendation of new product (i.e., target domain). However, although most of the existing solutions of transfer learning work well on predicting user ratings, few of them can be directly exploited for the top-k recommendation with respect to user preferences. To this end, in this paper, we propose a novel transfer learning approach based on metric learning and active learning to address the problem of top-k potential user recommendation. Specifically, by modeling the user behaviors on historical products, we first propose to learn a global distance metric for capturing the commonality of users and segmenting users into different neighborhoods. Furthermore, to enhance the local discriminability among different neighborhoods, we select one representative user for each neighborhood by a new type of active learning and also learn local distance metrics for better estimating the user preferences. With such representative selection and metric learning, a novel method is developed for recommending top-k potential users of new products. Finally, we perform extensive experiments on real-world datasets, and the results validate the effectiveness of the proposed methods.