Improved Questionnaire Trees for Active Learning in Recommender Systems

A key challenge in recommender systems is how to profile new-users. This problem is called cold-start problem or new-user problem. A well-known solution for this problem is to use active learning techniques and ask new users to rate a few items in order to reveal their preferences. Recently, questionnaire trees (tree structures) have been proposed to build such adaptive questionnaires. In this paper, we improve the questionnaire trees by splitting the nodes of the trees in a finer-grained fashion. Specifically, the nodes are split in a 6-way manner instead of 3-way split. Furthermore, we compare our approach to online updating and show that our method outperforms online updating in order to fold-in the new user into recommendation model. Finally, we develop three simple baselines based on the questionnaire trees and compare them against the state-of-the-art baseline to show that the newuser problem in recommender systems is tough and demands a mature solution.