Name
Abstract | ||
|---|---|---|
Search engine click-through data is a valuable source of implicit user feedback for relevance. However, not all user clicks are good indication of relevance. The clicks from search experts, who are more successful searching a query, tend to be more reliable in indicating document relevance than those of the non-experts. Therefore, knowing the expertise of search users is helpful to better understand their clicks. In this paper, we propose two probabilistic modelings of user expertise in the environment of web search. Inspired by the idea of evaluation metrics in classification, search users are treated as classifiers and result documents are viewed as the data samples to classify in our models. A click implies that the document is classified as relevant by the user. Therefore, the expertise of a user can be measured by how well he/she classifies the documents. We carry out experiments on a real-world click-through data of a Chinese search engine. The results show that modeling user expertise helps the click models with relevance inference, which also implies that our models are effective in identifying the user expertise. © 2013 Springer-Verlag. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-45068-6_33 | AIRS |
Keywords | Field | DocType |
Click-through data,User expertise,web search | Search engine,Information retrieval,Computer science,Inference,Probabilistic logic,Search analytics | Conference |
Volume | Issue | ISSN |
8281 LNCS | null | 16113349 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianli Xing | 1 | 23 | 2.81 |
| Yiqun Liu | 2 | 1592 | 136.51 |
| Min Zhang | 3 | 1658 | 134.93 |
| Shaoping Ma | 4 | 1544 | 126.00 |
| Kuo Zhang | 5 | 311 | 20.43 |