Name
Abstract | ||
|---|---|---|
Click models are developed to interpret clicks by making assumptions on how users browse the search result page. Most existing click models implicitly assume that all users are homogeneous and act in the same way when browsing the search results. However, a number of researches have shown that users have diverse behavioral patterns, which is also observed in this paper by eye-tracking experiments and click-through log analysis. As a uniform click model for all users can hardly capture the diverse click behavior, in this paper we incorporate user preferences into both a variety of existing click models and a novel click model. The experimental results on a large-scale click-through data set show consistent and significant performance improvement of the click models with user preferences integrated. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2505515.2505704 | CIKM |
Keywords | Field | DocType |
uniform click model,click-through log analysis,large-scale click-through data,incorporating user preference,user preference,click model,novel click model,existing click model,search result,diverse click behavior,diverse behavioral pattern | Behavioral pattern,Information retrieval,Computer science,Homogeneous,Click path,Click model,Performance improvement | Conference |
Citations | PageRank | References |
9 | 0.49 | 21 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianli Xing | 1 | 23 | 2.81 |
| Yiqun Liu | 2 | 1592 | 136.51 |
| Jian-yun Nie | 3 | 3681 | 238.61 |
| Min Zhang | 4 | 1658 | 134.93 |
| Shaoping Ma | 5 | 1544 | 126.00 |
| Kuo Zhang | 6 | 311 | 20.43 |