Bamshad Mobasher

Information Systems · 2000–2020

A researcher in the Science corpus: 15 papers; trusts 77 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 98 citations into it; #352 of 8,771 by trust here, #432 by citations
• Artificial Intelligence — 36 citations into it; #19,173 of 35,345 by trust here, #25,040 by citations
• Management Science and Operations Research — 27 citations into it; #5,163 of 11,946 by trust here, #2,799 by citations
• Signal Processing — 27 citations into it; #2,592 of 7,572 by trust here, #2,001 by citations
• Computer Networks and Communications — 24 citations into it; #1,516 of 9,886 by trust here, #1,714 by citations
• Recommender Systems — 18 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it; #12,773 of 21,575 by trust here, #17,258 by citations
• Natural Language Processing — 12 citations into it

Whom Bamshad Mobasher trusts overall

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

1. Thomas Hofmann
2. George Karypis
3. Rakesh Agrawal
4. J. Konstan
5. Jonathan L. Herlocker
6. Badrul Munir Sarwar
7. Ken Lang
8. Pang-Ning Tan
9. Rayadurgam Srikant
10. John Riedl

Who trusts Bamshad Mobasher overall

1. Yoon Ho Cho
2. Mohamed Koutheair Khribi
3. Gediminas Adomavicius
4. Minghong Fang
5. Dimitrios Pierrakos
6. Federico Michele Facca
7. Ihsan Günes
8. Robin D. Burke
9. Charu C. Aggarwal
10. Fidel Cacheda

Papers from Bamshad Mobasher's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness (2007) — 513 citations
• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019) — 333 citations
• Controlling Popularity Bias in Learning-to-Rank Recommendation (2017) — 447 citations
• Integrating Web Usage and Content Mining for More Effective Personalization (2000) — 323 citations
• Feedback Loop and Bias Amplification in Recommender Systems (2020) — 327 citations
• Context-aware music recommendation based on latenttopic sequential patterns (2012) — 314 citations
• Personalized recommendation in social tagging systems using hierarchical clustering (2008) — 597 citations
• Data Mining for Web Personalization (2007) — 381 citations
• Robustness of collaborative recommendation based on association rule mining (2007) — 151 citations
• Web search personalization with ontological user profiles (2007) — 421 citations
• Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems (2005) — 125 citations
• Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization (2004) — 482 citations
• A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis (2003) — 374 citations
• Effective personalization based on association rule discovery from web usage data (2001) — 543 citations
• Automatic personalization based on Web usage mining (2000) — 1,521 citations