Comparing Prediction Models for Active Learning in Recommender Systems

Recommender systems help web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected. Active learning for recommender systems has been proposed in the past, to acquire preference information from users. Early active learning methods for recommender systems used as underlying model either memory-based approaches or the aspect model. However, matrix factorization has been recently demonstrated (especially after the Netflix challenge) as being superior to memory-based approaches or the aspect model. Therefore, it is promising to develop active learning methods based on this prediction model. In this paper, we thoroughly compare matrix factorization with the aspect model to find out which one is more suitable for applying active learning in recommender systems. The results show that beside improving the accuracy of recommendations, the matrix factorization approach also results in drastically reduced user waiting times, i.e., the time that the users wait before being asked a new query. Therefore, it is an ideal choice for using active learning in real-world applications of recommender systems.