Paper Info
Title
Active learning for aspect model in recommender systems
Abstract
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 specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.
Year
DOI
Venue
2011
10.1109/CIDM.2011.5949431
Computational Intelligence and Data Mining
Keywords
Field
DocType
Bayes methods,belief networks,learning (artificial intelligence),recommender systems,active learning method,aspect model features,complicated Bayesian approach,prediction model,recommender system
Recommender system,Information overload,Active learning,Active learning (machine learning),Computer science,Artificial intelligence,Preference learning,Machine learning,Bayesian probability
Conference
ISBN
Citations 
PageRank 
978-1-4244-9926-7
12
0.67
References 
Authors
19
4
Name
Order
Citations
PageRank
Rasoul Karimi1494.18
Christoph Freudenthaler2185361.55
Alexandros Nanopoulos3185695.35
Lars Schmidt-Thieme43802216.58