Name
Abstract | ||
|---|---|---|
We propose a novel model for rank aggregation from pair-wise comparisons which accounts for a heterogeneous population of inconsistent users whose preferences are different mixtures of multiple shared ranking schemes. By connecting this problem to recent advances in the non-negative matrix factorization (NMF) literature, we develop an algorithm that can learn the underlying shared rankings with provable statistical and computational efficiency guarantees. We validate the approach using semi-synthetic and real world datasets. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Rank aggregation, nonnegative matrix factorization, extreme point finding, random projection |
Field | DocType | ISSN |
Pairwise comparison,Population,Pattern recognition,Ranking SVM,Ranking,Computer science,Matrix decomposition,Sorting,Artificial intelligence,Non-negative matrix factorization,Mixture model,Machine learning | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weicong Ding | 1 | 33 | 2.82 |
| Prakash Ishwar | 2 | 951 | 67.13 |
| Venkatesh Saligrama | 3 | 1350 | 112.74 |