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

Content Provider	Springer Nature Link
Author	Karimi, Rasoul Napoulos, Alexandros Schmidt Thieme, Lars
Copyright Year	2014
Abstract	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.
Starting Page	39
Ending Page	64
Page Count	26
File Format	PDF
ISSN	09241868
Journal	User Modeling and User-Adapted Interaction
Volume Number	25
Issue Number	1
e-ISSN	15731391
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-11-19
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Active learning Recommender systems Cold-start problem Matrix factorization User Interfaces and Human Computer Interaction Multimedia Information Systems Management of Computing and Information Systems
Content Type	Text
Resource Type	Article
Subject	Education Human-Computer Interaction Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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