Paper Info
Title
Utilizing Urls Position To Estimate Intrinsic Query-Url Relevance
Abstract
Query-URL relevance (QUR) is an important criterion to measure the quality of commercial search engines. However, the traditional way to collect high-quality QURs is time-consuming and labor-intensive since it is primarily based on human judges. To address these issues, numerous models have been studied to automatically infer the QURs. Unlike the prior studies in this literature, we first empirically analyze the correlation between multiple annotators' judgments on QURs and URL position in ranking lists. By doing so, we reveal and justify the potential impacts of URL position on inferring intrinsic QURs. Inspired by this finding, a position-sensitive model (PSM) is proposed to infer QURs more accurately. In contrast with most existing approaches that attempt to construct the direct relationship between QURs and the features characterizing query-URL pairs, PSM assumes that the QUR is connected with the features through URL position. We conducted the experiments in real search engine Baidu.com, and compared the experimental results to those of the typical methods used in similar tasks, reporting significant gains over click-through rate and the normalized discounted cumulative gains (NDCGs).
Year
DOI
Venue
2013
10.1109/ICDM.2013.20
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
Keywords
Field
DocType
Relevance, Seach Engines, Evaluation component
Data mining,Search engine,Normalization (statistics),Relevance feedback,Information retrieval,Ranking,Computer science,Feature extraction,Correlation,Artificial intelligence,Instrumental and intrinsic value,Machine learning
Conference
Volume
Issue
ISSN
null
null
1550-4786
Citations 
PageRank 
References 
1
0.37
26
Authors
6
Name
Order
Citations
PageRank
Xiaogang Han1683.90
Wenjun Zhou220722.34
Xing Jiang310.37
Hengjie Song41307.13
Ming Zhong510.37
Toyoaki Nishida61097196.19