A Group-Specific Recommender System-Reference-Cited by-同舟云学术

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A Group-Specific Recommender System

Published:2017-06-29 Issue:519 Volume:112 Page:1344-1353
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Bi Xuan¹,Qu Annie²^ORCID,Wang Junhui³,Shen Xiaotong¹⁴

Affiliation:

1. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL

2. Yunnan University, China, and Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL

3. Department of Mathematics, City University of Hong Kong, Hong Kong

4. School of Statistics, University of Minnesota, Minneapolis, MN

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2016.1219261

Reference42 articles.

1. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

2. Regression-based latent factor models

3. Convergence of the backfitting algorithm for additive models

4. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

5. Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systems;Expert Systems with Applications;2024-12

2. Individualized dynamic latent factor model for multi-resolutional data with application to mobile health;Biometrika;2024-04-08

3. A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously;Statistica Sinica;2024

4. Are Heterogeinity and Conflicting Preferences No Longer a Problem? Personality-Based Dynamic Clustering for Group Recommender Systems;2024

5. Recommender Systems: A Review;Journal of the American Statistical Association;2023-11-06

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