Roelof van Zwol,
Lluis Garcia Pueyo,
Mridul Muralidharan,
Börkur Sigurbjörnsson.

In: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval. (short paper)

Link: doi

Abstract

The research described in this paper forms the backbone of a service that enables the faceted search experience of the Yahoo! search engine. We introduce an approach for a machine learned ranking of entity facets based on user click feedback and features extracted from three different ranking sources. The objective of the learned model is to predict the click-through rate on an entity facet. In an empirical evaluation we compare the performance of gradient boosted decision trees (GBDT) against a linear combination of features on two different click feedback models using the raw click-through rate (CTR), and click over expected clicks (COEC). The results show a significant improvement in retrieval performance, in terms of discounted cumulated gain, when ranking entity facets with GBDT trained on the COEC model. Most notably this is true when evaluated against the CTR test set.

Bibtex

@inproceedings{1835662,
author = {van Zwol, Roelof
and Garcia Pueyo, Llu'{i}s
and Muralidharan, Mridul
and Sigurbj"{o}rnsson, B"{o}rkur},
title = {Machine learned ranking of entity facets},
booktitle = {SIGIR '10: Proceeding of the 33rd international ACM SIGIR conference
on Research and development in information retrieval},
year = {2010},
isbn = {978-1-4503-0153-4},
pages = {879--880},
location = {Geneva, Switzerland},
doi = {http://doi.acm.org/10.1145/1835449.1835662},
publisher = {ACM},
address = {New York, NY, USA},
}