Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICTAI.2011.182 | ICTAI |
Keywords | Field | DocType |
closed-form solution,prediction accuracy,test data,optimal selection,matrix factorization,optimal active learning,recommender system,optimal solution,recommender systems,active learning method,towards optimal,prediction model,active learning,mathematical model,prediction algorithms,predictive models,closed form solution,matrix decomposition,information overload,learning artificial intelligence | Recommender system,Data mining,Information overload,Active learning,Active learning (machine learning),Computer science,Matrix decomposition,Exploit,Artificial intelligence,Test data,Predictive modelling,Machine learning | Conference |
ISSN | Citations | PageRank |
1082-3409 | 13 | 0.67 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rasoul Karimi | 1 | 49 | 4.18 |
| Christoph Freudenthaler | 2 | 1853 | 61.55 |
| Alexandros Nanopoulos | 3 | 1856 | 95.35 |
| Lars Schmidt-Thieme | 4 | 3802 | 216.58 |