Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large preexisting user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

Abstract—Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal active learning selects a query that directly optimizes the expected error for the test data. This approach is applicable for prediction models in which this question can be answered in closed-form given the distribution of test data is known. Unfortunately, there are many tasks and models for which the optimal selection cannot efficiently be found in closed-form. Therefore, most of the active learning methods optimize different, non-optimal criteria, such as uncertainty. Nevertheless, in this paper we exploit the characteristics of matrix factorization, which leads to a closed-form solution and by being inspired from existing optimal active learning for the regression task, develop a method that approximates the optimal solution for recommender systems. Our results demonstrate that the proposed method improves the prediction accuracy of MF.

Abstract not found

Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. However, different from the classic active learning, users (the ”oracle”) are not always able to provide an answer for queries. The easiest way to solve this problem is to ask most popular items, i.e items which have received many ratings from training users. But it is static and presents the same items to all users regardless of the ratings they have provided so far. In this paper we propose a method that improves the most popular selection strategy using the characteristics of matrix factorization. It finds similar users to the new user in the latent space and then selects item which is most popular among the similar users. The experimental results show the proposed method outperforms the most popular method both in terms of error and the number of received ratings.