Improved neighborhood-based algorithms for large-scale recommender systems

Authors:
Andreas Töscher;Michael Jahrer;Robert Legenstein
Affiliations:
Technische Universitaet Graz, Graz, Austria;Technische Universitaet Graz, Graz, Austria;Technische Universitaet Graz, Graz, Austria
Venue:
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Year:
2008

Citing 4
Cited 8

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Restricted Boltzmann machines for collaborative filtering

Proceedings of the 24th international conference on Machine learning
Modeling relationships at multiple scales to improve accuracy of large recommender systems

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining

Collaborative filtering with temporal dynamics

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A collaborative filtering approach to ad recommendation using the query-ad click graph

Proceedings of the 18th ACM conference on Information and knowledge management
Collaborative filtering with temporal dynamics

Communications of the ACM
Combining predictions for accurate recommender systems

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic definition of item similarity

Proceedings of the fifth ACM conference on Recommender systems
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems

Proceedings of the 7th ACM conference on Recommender systems
Multi-Criteria Recommender Systems based on Multi-Attribute Decision Making

Proceedings of International Conference on Information Integration and Web-based Applications & Services
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols

User Modeling and User-Adapted Interaction

Quantified Score

Hi-index	0.02

Visualization

Abstract

Neighborhood-based algorithms are frequently used modules of recommender systems. Usually, the choice of the similarity measure used for evaluation of neighborhood relationships is crucial for the success of such approaches. In this article we propose a way to calculate similarities by formulating a regression problem which enables us to extract the similarities from the data in a problem-specific way. Another popular approach for recommender systems is regularized matrix factorization (RMF). We present an algorithm -- neighborhood-aware matrix factorization -- which efficiently includes neighborhood information in a RMF model. This leads to increased prediction accuracy. The proposed methods are tested on the Netflix dataset.