Name
Abstract | ||
|---|---|---|
Pseudo-relevance feedback (PRF) is a technique commonly used in the field of information retrieval. The performance of PRF is heavily dependent upon parameter values. When relevance judgements are unavailable, these parameters are difficult to set. In the following paper, we introduce a novel approach to PRF inspired by collaborative filtering (CF). We also describe an adaptive tuning method which automatically sets algorithmic parameters. In a multi-stage evaluation using publicly available datasets, our technique consistently outperforms conventional PRF, regardless of the underlying retrieval model. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2013.06.030 | Expert Syst. Appl. |
Keywords | Field | DocType |
novel approach,collaborative pseudo-relevance feedback,conventional prf,underlying retrieval model,information retrieval,algorithmic parameter,following paper,available datasets,pseudo-relevance feedback,multi-stage evaluation,adaptive tuning method,collaborative filtering | Data mining,Relevance feedback,Collaborative filtering,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
40 | 17 | 0957-4174 |
Citations | PageRank | References |
7 | 0.47 | 47 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Zhou | 1 | 342 | 25.99 |
| Mark Truran | 2 | 286 | 14.43 |
| Jianxun Liu | 3 | 640 | 67.12 |
| Sanrong Zhang | 4 | 10 | 1.53 |