研究论文
Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert Bell, Chris Volinsky
Yahoo (United States) AT&T (United States)
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摘要与影响
摘要 · 完整
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
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学术脉络
学科主题
计算机 / AIRecommender Systems and Techniques
Image Retrieval and Classification Techniques · Consumer Market Behavior and Pricing
参考文献 12
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
被引 703Y. Zhou, Dennis M. Wilkinson, Robert Schreiber · Lecture notes in computer science · 2008
Application of Dimensionality Reduction in Recommender System - A Case Study
被引 1,496Badrul Sarwar, George Karypis, Joseph A. Konstan · 2000
Using collaborative filtering to weave an information tapestry
被引 4,088David Theo Goldberg, David M. Nichols, Brian Oki · Communications of the ACM · 1992
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引用本文 11,910
LightGCN
被引 4,354Xiangnan He, Kuan Deng, Xiang Wang · 2020
Geometric Deep Learning: Going beyond Euclidean data
被引 3,704Michael M. Bronstein, Joan Bruna, Yann LeCun · IEEE Signal Processing Magazine · 2017
Neural Graph Collaborative Filtering
被引 3,168Xiang Wang, Xiangnan He, Meng Wang · 2019
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