Himan Abdollahpouri

Information Systems · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 8 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 34 citations into it; #3,223 of 8,771 by trust here, #4,012 by citations
• Artificial Intelligence — 28 citations into it; #23,404 of 35,345 by trust here, #28,481 by citations
• Management Science and Operations Research — 18 citations into it; #5,103 of 11,946 by trust here, #4,746 by citations
• Recommender Systems — 11 citations into it
• Safety Research — 11 citations into it

Whom Himan Abdollahpouri trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Sirui Yao
2. Gediminas Adomavicius
3. Paul Resnick
4. Hongzhi Yin
5. Bert Huang
6. Robin D. Burke
7. Chen Karako
8. Thomas Hofmann
9. Òscar Celma
10. F. Maxwell Harper

Who trusts Himan Abdollahpouri overall

1. Yang Zhang
2. Fuli Feng
3. Tianxin Wei
4. Jiawei Chen
5. Xiangnan He
6. Robin D. Burke
7. Bamshad Mobasher
8. Masoud Mansoury
9. Hande Dong

Papers from Himan Abdollahpouri's perspective

Ordered by how strongly the researchers Himan Abdollahpouri trusts endorse each paper. Same corpus, a different order for every researcher.

1. Equality of Opportunity in Supervised Learning
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Attention is All you Need (Neural Information Processing Systems)
6. Empirical Analysis of Predictive Algorithms for Collaborative Filtering
7. Long Short-Term Memory
8. Discrimination-aware data mining
9. Improving recommendation lists through topic diversification
10. An open architecture for collaborative filtering of netnews

Their papers

• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019) — 333 citations
• Controlling Popularity Bias in Learning-to-Rank Recommendation (2017) — 447 citations
• Feedback Loop and Bias Amplification in Recommender Systems (2020) — 327 citations
• Multistakeholder recommendation: Survey and research directions (2020) — 301 citations