| Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling L Guo, CJ Vargo, Z Pan, W Ding, P Ishwar Journalism & Mass Communication Quarterly 93 (2), 332-359, 2016 | 325 | 2016 |
| Dynamic word embeddings for evolving semantic discovery Z Yao, Y Sun, W Ding, N Rao, H Xiong Proceedings of the eleventh acm international conference on web search and …, 2018 | 323 | 2018 |
| Just One More: Modeling Binge Watching Behavior W Trouleau, A Ashkan, W Ding, B Eriksson 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2016 | 79 | 2016 |
| Topic discovery through data dependent and random projections W Ding, MH Rohban, P Ishwar, V Saligrama International Conference on Machine Learning, 1202-1210, 2013 | 69 | 2013 |
| A sparse topic model for extracting aspect-specific summaries from online reviews V Rakesh, W Ding, A Ahuja, N Rao, Y Sun, CK Reddy Proceedings of the 2018 World Wide Web Conference, 1573-1582, 2018 | 30 | 2018 |
| Efficient distributed topic modeling with provable guarantees W Ding, M Rohban, P Ishwar, V Saligrama Artificial Intelligence and Statistics, 167-175, 2014 | 27 | 2014 |
| A topic modeling approach to ranking W Ding, P Ishwar, V Saligrama Artificial Intelligence and Statistics, 214-222, 2015 | 25 | 2015 |
| Node Embedding via Word Embedding for Network Community Discovery W Ding, C Lin, P Ishwar arXiv:1611.03028, 2016 | 21 | 2016 |
| Whole page optimization with global constraints W Ding, D Govindaraj, SVN Vishwanathan Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 20 | 2019 |
| Most large topic models are approximately separable W Ding, P Ishwar, V Saligrama 2015 Information theory and applications workshop (ITA), 199-203, 2015 | 19 | 2015 |
| Necessary and sufficient conditions for novel word detection in separable topic models W Ding, P Ishwar, MH Rohban, V Saligrama arXiv preprint arXiv:1310.7994, 2013 | 15 | 2013 |
| Learning mixed membership mallows models from pairwise comparisons W Ding, P Ishwar, V Saligrama arXiv preprint arXiv:1504.00757, 2015 | 14 | 2015 |
| A simple approach to learn polysemous word embeddings Y Sun, N Rao, W Ding arXiv preprint arXiv:1707.01793, 2017 | 13 | 2017 |
| Matrix Factorization with Side and Higher Order Information V Shah, N Rao, W Ding https://arxiv.org/abs/1705.02047, 2017 | 8 | 2017 |
| A new geometric approach to latent topic modeling and discovery W Ding, MH Rohban, P Ishwar, V Saligrama 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 8 | 2013 |
| A topic modeling approach to rank aggregation W Ding, P Ishwar, V Saligrama Advances in on Neural Information Processing Systems, workshop on Analysis …, 2014 | 7 | 2014 |
| Necessary and sufficient conditions and a provably efficient algorithm for separable topic discovery W Ding, P Ishwar, V Saligrama arXiv preprint arXiv:1508.05565, 2015 | 6 | 2015 |
| Node embedding for network community discovery C Lin, P Ishwar, W Ding 2017 IEEE international conference on acoustics, speech and signal …, 2017 | 4 | 2017 |
| Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach Y Zhang, F Tao, X Liu, R Su, X Mei, W Ding, Z Zhao, L Yuan, J Liu arXiv preprint arXiv:2010.12662, 2020 | 3 | 2020 |
| Appliance failure prediction BC Eriksson, A Pudhiyaveetil, Y Sun, W Ding US Patent App. 16/004,353, 2018 | 3 | 2018 |
Prakash IshwarProfessor of Electrical and Computer Engineering at Boston UniversityVerified email at bu.edu
