Name
Abstract | ||
|---|---|---|
We propose the use of learning to rank techniques to shorten query sessions by maximizing the probability that the query we predict is the "final" query of the current search session. We present a preliminary evaluation showing that this approach is a promising research direction. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2487788.2487851 | WWW (Companion Volume) |
Keywords | Field | DocType |
preliminary evaluation,promising research direction,query session,current search session,learning to rank | Learning to rank,Data mining,Computer science,Web query classification,Ranking (information retrieval),Artificial intelligence,Query optimization,Web search query,Information retrieval,Query expansion,Sargable,Online aggregation,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-2038-2 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina Ioana Muntean | 1 | 32 | 8.28 |
| Franco Maria Nardini | 2 | 314 | 36.52 |
| Fabrizio Silvestri | 3 | 1819 | 107.29 |
| Marcin Sydow | 4 | 264 | 22.71 |