Ground truth labels are one of the most important parts in many test collections for information retrieval. Each label, depicting the relevance between a query-document pair, is usually judged by a human, and this process is time-consuming and labor-intensive. Automatically Generating labels from click-through data has attracted increasing attention. In this paper, we propose a Unified Click Model to predict the multi-level labels, which aims at comprehensively considering the advantages of the Position Models and Cascade Models. Experiments show that the proposed click model outperforms the existing click models in predicting the multi-level labels, and could replace the labels judged by humans for test collections.
Description
Automatically generating labels based on unified click model
%0 Conference Paper
%1 hua2011automatically
%A Hua, Guichun
%A Zhang, Min
%A Liu, Yiqun
%A Ma, Shaoping
%A Ru, Liyun
%B Proceedings of the 20th international conference companion on World wide web
%C New York, NY, USA
%D 2011
%I ACM
%K check evaluation implicit-feedback model
%P 59--60
%R 10.1145/1963192.1963223
%T Automatically generating labels based on unified click model
%U http://doi.acm.org/10.1145/1963192.1963223
%X Ground truth labels are one of the most important parts in many test collections for information retrieval. Each label, depicting the relevance between a query-document pair, is usually judged by a human, and this process is time-consuming and labor-intensive. Automatically Generating labels from click-through data has attracted increasing attention. In this paper, we propose a Unified Click Model to predict the multi-level labels, which aims at comprehensively considering the advantages of the Position Models and Cascade Models. Experiments show that the proposed click model outperforms the existing click models in predicting the multi-level labels, and could replace the labels judged by humans for test collections.
%@ 978-1-4503-0637-9
@inproceedings{hua2011automatically,
abstract = {Ground truth labels are one of the most important parts in many test collections for information retrieval. Each label, depicting the relevance between a query-document pair, is usually judged by a human, and this process is time-consuming and labor-intensive. Automatically Generating labels from click-through data has attracted increasing attention. In this paper, we propose a Unified Click Model to predict the multi-level labels, which aims at comprehensively considering the advantages of the Position Models and Cascade Models. Experiments show that the proposed click model outperforms the existing click models in predicting the multi-level labels, and could replace the labels judged by humans for test collections.},
acmid = {1963223},
added-at = {2011-07-29T16:45:33.000+0200},
address = {New York, NY, USA},
author = {Hua, Guichun and Zhang, Min and Liu, Yiqun and Ma, Shaoping and Ru, Liyun},
biburl = {https://www.bibsonomy.org/bibtex/2a21c5f5a5032ea8c3528e68d4690fa50/beate},
booktitle = {Proceedings of the 20th international conference companion on World wide web},
description = {Automatically generating labels based on unified click model},
doi = {10.1145/1963192.1963223},
interhash = {06941cc19ae7a6b3273484b9ac638a0e},
intrahash = {a21c5f5a5032ea8c3528e68d4690fa50},
isbn = {978-1-4503-0637-9},
keywords = {check evaluation implicit-feedback model},
location = {Hyderabad, India},
numpages = {2},
pages = {59--60},
publisher = {ACM},
series = {WWW '11},
timestamp = {2011-07-29T16:45:33.000+0200},
title = {Automatically generating labels based on unified click model},
url = {http://doi.acm.org/10.1145/1963192.1963223},
year = 2011
}