Name
Abstract | ||
|---|---|---|
Understanding the temporal orientation of web search queries is an important issue for the success of information access systems. In this paper, we propose a multi-objective ensemble learning solution that (1) allows to accurately classify queries along their temporal intent and (2) identifies a set of performing solutions thus offering a wide range of possible applications. Experiments show that correct representation of the problem can lead to great classification improvements when compared to recent state-of-the-art solutions and baseline ensemble techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2766462.2767792 | International Conference on Research an Development in Information Retrieval |
Keywords | Field | DocType |
Temporal IR,Ensemble learning,Multi-objective optimization | Data mining,Temporal orientation,Information retrieval,Computer science,Information access,Multi-objective optimization,Artificial intelligence,Ensemble learning,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Hasanuzzaman | 1 | 52 | 13.52 |
| Sriparna Saha | 2 | 1064 | 106.07 |
| Gaël Dias | 3 | 354 | 41.95 |
| Stéphane Ferrari | 4 | 5 | 3.16 |