Name
Abstract | ||
|---|---|---|
In information retrieval systems and digital libraries, retrieval result evaluation is a very important aspect. Up to now, almost all commonly used metrics such as average precision and recall level precision are ranking based metrics. In this work, we investigate if it is a good option to use a score based method, the Euclidean distance, for retrieval evaluation. Two variations of it are discussed: one uses the linear model to estimate the relation between rank and relevance in resultant lists, and the other uses a more sophisticated cubic regression model for this. Our experiments with two groups of submitted results to TREC demonstrate that the introduced new metrics have strong correlation with ranking based metrics when we consider the average of all 50 queries. On the other hand, our experiments also show that one of the variations (the linear model) has better overall quality than all those ranking based metrics involved. Another surprising finding is that a commonly used metric, average precision, may not be as good as previously thought. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24577-0_9 | BNCOD |
Keywords | Field | DocType |
euclidean distance,retrieval evaluation,information retrieval system,new metrics,retrieval result evaluation,sophisticated cubic regression model,recall level precision,linear model,good option,average precision | Data mining,Ranking,Linear model,Computer science,Euclidean distance,Precision and recall,Polynomial regression,Correlation,Artificial intelligence,Digital library,Machine learning | Conference |
Volume | ISSN | Citations |
7051 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Wu | 1 | 370 | 33.55 |
| Yaxin Bi | 2 | 541 | 47.76 |
| Xiaoqin Zeng | 3 | 407 | 32.97 |