English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In Online Learning to Rank (OLTR) the aim is to find an optimal ranking modelby interacting with users. When learning from user behavior, systems mustinteract with users while simultaneously learning from those interactions.Unlike other Learning to Rank (LTR) settings, existing research in this fieldhas been limited to linear models. This is due to the speed-quality tradeoffthat arises when selecting models: complex models are more expressive and canfind the best rankings but need more user interactions to do so, a requirementthat risks frustrating users during training. Conversely, simpler models can beoptimized on fewer interactions and thus provide a better user experience, butthey will converge towards suboptimal rankings. This tradeoff creates adeadlock, since novel models will not be able to improve either the userexperience or the final convergence point, without sacrificing the other. Ourcontribution is twofold. First, we introduce a fast OLTR model called Sim-MGDthat addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranksdocuments based on similarities with reference documents. It converges rapidlyand, hence, gives a better user experience but it does not converge towards theoptimal rankings. Second, we contribute Cascading Multileave Gradient Descent(C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using acascade that enables combinations of the best of two worlds: fast learning andhigh quality final convergence. C-MGD can provide the better user experience ofSim-MGD while maintaining the same convergence as the state-of-the-art MGDmodel. This opens the door for future work to design new models for OLTRwithout having to deal with the speed-quality tradeoff.

Balancing Speed and Quality in Online Learning to Rank for Information Retrieval