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  1. Data Mining and Knowledge Discovery
  2. Data Mining and Knowledge Discovery : Volume 30
  3. Data Mining and Knowledge Discovery : Volume 30, Issue 5, September 2016
  4. Bayesian Wishart matrix factorization
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Data Mining and Knowledge Discovery : Volume 31
Data Mining and Knowledge Discovery : Volume 30
Data Mining and Knowledge Discovery : Volume 30, Issue 6, November 2016
Data Mining and Knowledge Discovery : Volume 30, Issue 5, September 2016
Guest editors’ introduction to the EcmlPkdd 2016 journal track special issue of Machine Learning
Irrevocable-choice algorithms for sampling from a stream
A distributed approach for graph mining in massive networks
Generalized random shapelet forests
Skopus: Mining top-k sequential patterns under leverage
Using regression makes extraction of shared variation in multiple datasets easy
Top-k overlapping densest subgraphs
Bayesian Wishart matrix factorization
Ensembles of label noise filters: a ranking approach
Locating the contagion source in networks with partial timestamps
Mining rooted ordered trees under subtree homeomorphism
Scalable time series classification
C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content
ClusPath: a temporal-driven clustering to infer typical evolution paths
An efficient exact algorithm for triangle listing in large graphs
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Optimizing network robustness by edge rewiring: a general framework
Data Mining and Knowledge Discovery : Volume 30, Issue 4, July 2016
Data Mining and Knowledge Discovery : Volume 30, Issue 3, May 2016
Data Mining and Knowledge Discovery : Volume 30, Issue 2, March 2016
Data Mining and Knowledge Discovery : Volume 30, Issue 1, January 2016
Data Mining and Knowledge Discovery : Volume 29
Data Mining and Knowledge Discovery : Volume 28
Data Mining and Knowledge Discovery : Volume 27
Data Mining and Knowledge Discovery : Volume 26
Data Mining and Knowledge Discovery : Volume 25
Data Mining and Knowledge Discovery : Volume 24
Data Mining and Knowledge Discovery : Volume 23
Data Mining and Knowledge Discovery : Volume 22
Data Mining and Knowledge Discovery : Volume 21
Data Mining and Knowledge Discovery : Volume 20
Data Mining and Knowledge Discovery : Volume 19
Data Mining and Knowledge Discovery : Volume 18
Data Mining and Knowledge Discovery : Volume 17
Data Mining and Knowledge Discovery : Volume 16
Data Mining and Knowledge Discovery : Volume 15
Data Mining and Knowledge Discovery : Volume 14
Data Mining and Knowledge Discovery : Volume 13
Data Mining and Knowledge Discovery : Volume 12
Data Mining and Knowledge Discovery : Volume 11
Data Mining and Knowledge Discovery : Volume 10
Data Mining and Knowledge Discovery : Volume 9
Data Mining and Knowledge Discovery : Volume 8
Data Mining and Knowledge Discovery : Volume 7
Data Mining and Knowledge Discovery : Volume 6
Data Mining and Knowledge Discovery : Volume 5
Data Mining and Knowledge Discovery : Volume 4
Data Mining and Knowledge Discovery : Volume 3
Data Mining and Knowledge Discovery : Volume 2
Data Mining and Knowledge Discovery : Volume 1

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Bayesian Wishart matrix factorization

Content Provider Springer Nature Link
Author Luo, Cheng Cai, Xiongcai
Copyright Year 2016
Abstract User tastes are constantly drifting over time as users are exposed to different types of products. The ability to model the tendency of both user preferences and product attractiveness is vital to the success of recommender systems (RSs). We propose a Bayesian Wishart matrix factorization method to model the temporal dynamics of variations among user preferences and item attractiveness in a novel algorithmic perspective. The proposed method is able to well model and properly control diverse rating behaviors across time frames and related temporal effects within time frames in the tendency of user preferences and item attractiveness. We evaluate the proposed method on two synthetic and three real-world benchmark datasets for RSs. Experimental results demonstrate that our proposed method significantly outperforms a variety of state-of-the-art methods in RSs.
Starting Page 1166
Ending Page 1191
Page Count 26
File Format PDF
ISSN 13845810
Journal Data Mining and Knowledge Discovery
Volume Number 30
Issue Number 5
e-ISSN 1573756X
Language English
Publisher Springer US
Publisher Date 2016-08-04
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Recommender systems Temporal dynamics Matrix factorization Data Mining and Knowledge Discovery Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Content Type Text
Resource Type Article
Subject Computer Networks and Communications Information Systems Computer Science Applications
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