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).
['Xiaogang Han', 'Wenjun Zhou', 'Xing Jiang', 'Hengjie Song', 'Ming Zhong', 'Toyoaki Nishida']
Utilizing URLs Position to Estimate Intrinsic Query-URL Relevance