Dynamic adaptation of hypertext structure
HYPERTEXT '91 Proceedings of the third annual ACM conference on Hypertext
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Empirical Study of Recommender Systems Using Linear Classifiers
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Guest Editors' Introduction: Recommender Systems
IEEE Intelligent Systems
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Evaluation of attribute-aware recommender system algorithms on data with varying characteristics
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
T-Recs: Time-aware Twitter-based Drug Recommender System
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
| Hi-index | 0.00 |
Recommender systems recommend products to customers based on ratings or past customer behavior. Without any information about attributes of the products or customers involved, the problem has been tackled most successfully by a nearest neighbor method called collaborative filtering in the context, while additional efforts invested in building classification models did not pay off and did not increase the quality. Therefore, classification methods have mainly been used in conjunction with product or customer attributes. Starting from a view on the plain recommendation task without attributes as a multi-class classification problem, we investigate two particularities, its autocorrelation structure as well as the absence of re-occurring items (repeat buying). We adapt the standard generic reductions 1-vs-rest and 1-vs-1 of multi-class problems to a set of binary classification problems to these particularities and thereby provide a generic compound classifier for recommender systems. We evaluate a particular specialization thereof using linear support vector machines as member classifiers on MovieLens data and show that it outperforms state-of-the-artmethods, i.e., item-based collaborative filtering.