A supervised active learning framework for recommender systems based on decision trees

A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to ask new users to rate a few items to reveal their preferences and to use active learning to find optimally informative items. Compared to the application of active learning in classification (regression), active learning in recommender systems presents several differences: although there are no ratings for new users, there is an abundance of available ratings—collectively—from past (existing) users. In this paper, we propose an innovative approach for active learning in recommender systems, which aims at taking advantage of this additional information. The main idea is to consider existing users as (hypothetical) new users and solve an active learning problem for each of them. In the end, we aggregate all solved problems in order to learn how to solve the active learning problem for a real new user. As the ratings of existing users (i.e., labels) are known and are used for active learning purposes, the proposed framework is in fact a supervised active learning framework. Based on this framework, we investigate two different types of models: the first model is based on information about average item ratings and the second on matrix factorization. We present experimental results on the Netflix dataset, which show that the proposed approach significantly outperforms state-of-the-art baselines.