Name
Abstract | ||
|---|---|---|
Learning to rank plays an important role in information retrieval. In most of the existing solutions for learning to rank, all the queries with their returned search results are learnt and ranked with a single model. In this paper, we demonstrate that it is highly beneficial to divide queries into multiple groups and conquer search ranking based on query difficulty. To this end, we propose a method which first characterizes a query using a variety of features extracted from user search behavior, such as the click entropy, the query reformulation probability. Next, a classification model is built on these extracted features to assign a score to represent how difficult a query is. Based on this score, our method automatically divides queries into groups, and trains a specific ranking model for each group to conquer search ranking. Experimental results on RankSVM and RankNet with a large-scale evaluation dataset show that the proposed method can achieve significant improvement in the task of web search ranking. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1645953.1646255 | CIKM |
Keywords | Field | DocType |
single model,classification model,query reformulation probability,specific ranking model,search ranking,query difficulty,web search ranking,user search behavior,search result,learning to rank,divide and conquer,information retrieval,feature extraction | Data mining,Learning to rank,Query language,Ranking SVM,Computer science,Web query classification,Ranking (information retrieval),Artificial intelligence,Query optimization,Web search query,Information retrieval,Query expansion,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.41 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyuan Allen Zhu | 1 | 684 | 46.35 |
| Weizhu Chen | 2 | 597 | 38.77 |
| Tao Wan | 3 | 181 | 21.18 |
| Chenguang Zhu | 4 | 328 | 22.92 |
| Gang Wang | 5 | 521 | 25.88 |
| Zheng Chen | 6 | 5019 | 256.89 |