Name
Abstract | ||
|---|---|---|
Expert finding is the process of identifying experts given a particular topic. In this paper, we propose a method called Learning to Rank for Expert Finding LREF attempting to leverage learning to rank to improve the estimation for expert finding. Learning to rank is an established means of predicting ranking and has recently demonstrated high promise in information retrieval. LREF first defines representations for both topics and experts, and then collects the existing popular language models and basic document features to form feature vectors for learning purpose from the representations. Finally, LRER adopts RankSVM, a pair wise learning to rank algorithm, to generate the lists of experts for topics. Extensive experiments in comparison with the language models profile based model and document based model, which are state-of-the-art expert finding methods, show that LREF enhances expert finding accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-53917-6_20 | ADMA |
Keywords | Field | DocType |
Expert finding,Features,Language model,Learning to rank | Learning to rank,Data mining,Feature vector,Ranking,Computer science,Artificial intelligence,Language model,Machine learning | Conference |
Volume | Issue | Citations |
8347 LNAI | PART 2 | 2 |
PageRank | References | Authors |
0.36 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Li Qi | 2 | 345 | 67.01 |
| Jiang Y. | 3 | 8 | 2.96 |
| Xia Shu-Tao | 4 | 342 | 75.29 |
| Zhang Lan-Shan | 5 | 2 | 0.69 |