Query performance prediction for aspect weighting in search result diversification

Date

2014-01-01

Author

Ozdemiray, Ahmet Murat
Altıngövde, İsmail Sengör

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

6
views

0
downloads

Cite This

Accurate estimation of query aspect weights is an important issue to improve the performance of explicit search result diversification algorithms. For the first time in the literature, we propose using post-retrieval query performance predictors (QPPs) to estimate, for each aspect, the retrieval effectiveness on the candidate document set, and leverage these estimations to set the aspect weights. In addition to utilizing well-known QPPs from the literature, we also introduce three new QPPs that are based on score distributions and hence, can be employed for online query processing in real-life search engines. Our exhaustive experiments reveal that using QPPs for aspect weighting improves almost all state-of-the-art diversification algorithms in comparison to using a uniform weight estimator. Furthermore, the proposed QPPs are comparable or superior to the existing predictors in the context of aspect weighting.

Subject Keywords

İnformation systems

URI

https://hdl.handle.net/11511/38332

Collections

Department of Computer Engineering, Conference / Seminar