Learning to Rank: A Progressive Neural Network Learning Approach
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Publisher | IEEE |
| Pages | 8355-8359 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-4799-8131-1 |
| ISBN (Print) | 978-1-4799-8132-8 |
| DOIs | |
| Publication status | Published - 17 Apr 2019 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Acoustics, Speech and Signal Processing - Duration: 1 Jan 1900 → 1 Jan 2000 |
Publication series
| Name | IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| ISSN (Print) | 1520-6149 |
| ISSN (Electronic) | 2379-190X |
Conference
| Conference | IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| Period | 1/01/00 → 1/01/00 |
Abstract
Learning to rank is an essential component in an information retrieval system. The state-of-the-art ranking systems are often based on an ensemble of classifiers, such as Random Forest or LambdaMART, which aggregates the ranking outputs produced by thousands of classifiers. The storage and computation requirement of an ensemble model is usually very high, imposing a significant operating cost to the retrieval system. To tackle this problem, we propose an algorithm that adaptively learns a single heterogeneous feedforward network architecture, composing of Generalized Operational Perceptrons, given a ranking problem. Experimental results in web search ranking and image retrieval tasks show that the proposed algorithm compares favourably to the related algorithms.
Keywords
- feedforward neural nets, image retrieval, learning (artificial intelligence), learning to rank, random forest, storage requirement, Web search ranking, progressive neural network learning approach, LambdaMART, image retrieval tasks, ranking problem, Generalized Operational Perceptrons, single heterogeneous feedforward network architecture, significant operating cost, ensemble model, computation requirement, state-of-the-art ranking systems, information retrieval system, essential component, Generalized Operational Perceptron, Progressive Neural Network Learning