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UvA-DARE ( Digital Academic Repository ) Large Scale Co-Regularized Ranking
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tsivtsivadze Hüllermeyer |
| Copyright Year | 2012 |
| Abstract | As unlabeled data is usually easy to collect, semisupervised learning algorithms that can be trained on large amounts of unlabeled and labeled data are becoming increasingly popular for ranking and preference learning problems [6, 23, 8, 21]. However, the computational complexity of the vast majority of these (pairwise) ranking and preference learning methods is super-linear, as optimizing an objective function over all possible pairs of data points is computationally expensive. This paper builds upon [16] and proposes a novel large scale co-regularized algorithm that can take unlabeled data into account. This algorithm is suitable for learning to rank when large amounts of labeled and unlabeled data are available for training. Most importantly, the complexity of our algorithm does not depend on the size of the dataset. We evaluate the proposed algorithm using several publicly available datasets from the information retrieval (IR) domain, and show that it improves performance over supervised methods. Finally, we discuss possible implications of our algorithm for learning with implicit feedback in an online setting. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://pure.uva.nl/ws/files/2524801/164843_PL12_Proceedings.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
