Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Scouts, promoters, and connectors: The roles of ratings in nearest-neighbor collaborative filtering
ACM Transactions on the Web (TWEB)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
Matrix factorization techniques for context aware recommendation
Proceedings of the fifth ACM conference on Recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Line orthogonality in adjacency eigenspace with application to community partition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Mind the eigen-gap, or how to accelerate semi-supervised spectral learning algorithms
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hypergraph learning with hyperedge expansion
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
| Hi-index | 0.00 |
Matrix factorization techniques such as the singular value decomposition (SVD) have had great success in recommender systems. We present a new perspective of SVD for constructing a latent space from the training data, which is justified by the theory of hypergraph model. We show that the vectors representing the items in the latent space can be grouped into (approximately) orthogonal clusters which correspond to the vertex clusters in the co-rating hypergraph, and the lengths of the vectors are indicators of the representativeness of the items. These properties are used for making top-$N$ recommendations in a two-phase algorithm. In this work, we provide a new explanation for the significantly better performance of the asymmetric SVD approaches and a novel algorithm for better diversity in top-N recommendations.