Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2014

Abstract

Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced from multiple rank learners. Base on the TREC microblog datasets, we found that our selection-based ensemble approach can significantly outperform using the single best ranker, and it also has clear advantage over the rank fusion that combines the results of all the available models.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of SIGIR 2014 Workshop on Social Media Retrieval and Analysis (SoMeRA 2014)

First Page

21

Last Page

26

Identifier

10.1145/2632188.2632202

Publisher

ACM Press

City or Country

Gold Coast, Queensland, Australia

Additional URL

https://doi.org/10.1145/2632188.2632202

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