Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2015

Abstract

Traditional learning to rank methods learn ranking models from training data in a batch and offline learning mode, which suffers from some critical limitations, e.g., poor scalability as the model has to be retrained from scratch whenever new training data arrives. This is clearly nonscalable for many real applications in practice where training data often arrives sequentially and frequently. To overcome the limitations, this paper presents SOLAR- a new framework of Scalable Online Learning Algorithms for Ranking, to tackle the challenge of scalable learning to rank. Specifically, we propose two novel SOLAR algorithms and analyze their IR measure bounds theoretically. We conduct extensive empirical studies by comparing our SOLAR algorithms with conventional learning to rank algorithms on benchmark testbeds, in which promising results validate the efficacy and scalability of the proposed novel SOLAR algorithms.

Discipline

Computer Sciences | Databases and Information Systems | Theory and Algorithms

Publication

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015, 26-31 July, Beijing

Volume

1

First Page

1692

Last Page

1701

ISBN

9781941643723

Identifier

10.3115/v1/P15-1163

Publisher

ACL

City or Country

Beijing, China

Additional URL

https://doi.org/10.3115/v1/P15-1163

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