Linas Baltrunas

Information Systems · 2010–2015

A researcher in the Science corpus: 8 papers; trusts 41 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 80 citations into it; #451 of 8,771 by trust here, #341 by citations
• Artificial Intelligence — 42 citations into it; #5,611 of 35,345 by trust here, #5,390 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Management Science and Operations Research — 29 citations into it; #864 of 11,946 by trust here, #510 by citations
• Recommender Systems — 28 citations into it; #60 of 233 by trust here, #49 by citations
• Marketing — 19 citations into it
• Information Retrieval & Search — 13 citations into it
• Natural Language Processing — 10 citations into it

Whom Linas Baltrunas trusts overall

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

1. Yehuda Koren
2. Gediminas Adomavicius
3. Robin D. Burke
4. Alexandros Karatzoglou
5. Markus Weimer
6. Thomas Hofmann
7. Anthony David Jameson
8. Don Coppersmith
9. Alexander Tuzhilin
10. Cynthia Dwork

Who trusts Linas Baltrunas overall

1. Yue Shi
2. Alexandros Karatzoglou
3. Balázs Hidasi
4. Steffen Rendle
5. Charu C. Aggarwal
6. Nan Wang
7. Fajie Yuan
8. Bin Guo
9. Harald Steck
10. Mengting Wan

Papers from Linas Baltrunas's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012) — 339 citations
• Recurrent neural networks (2013) — 997 citations
• Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering (2010) — 860 citations
• Session-based Recommendations with Recurrent Neural Networks (2015) — 2,897 citations
• TFMAP: optimizing MAP for top-n context-aware recommendation (2012) — 231 citations
• Group recommendations with rank aggregation and collaborative filtering (2010) — 547 citations
• Learning to rank for recommender systems (2013) — 154 citations
• Matrix factorization techniques for context aware recommendation (2011) — 378 citations