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Ordrank: learning to rank with ordered multiple hyperplanes.
| Content Provider | CiteSeerX |
|---|---|
| Author | Sun, Heli Huang, Jianbin Feng, Boqin Li, Tao Zhao, Yingliang Liu, Jun |
| Abstract | Abstract—Ranking is a central problem for information retrieval systems, because the performance of an information retrieval system is mainly evaluated by the effectiveness of its ranking results. Learning to rank has received much attention in recent years due to its importance in information retrieval. This paper focuses on learning to rank in document retrieval and presents a ranking model named OrdRank that ranks documents with ordered multiple hyperplanes. Comparison of OrdRank with other state-of-the-art ranking techniques is conducted and several evaluation criteria are employed to evaluate its performance. Experimental results on the OHSUMED dataset show that OrdRank outperforms other methods, both in terms of quality of ranking results and efficiency. Keywords-learning to rank; order; multiple hyperplanes I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Ordered Multiple Hyperplanes Information Retrieval System Ohsumed Dataset Show Central Problem Multiple Hyperplanes Document Retrieval Recent Year Abstract Ranking Several Evaluation Criterion State-of-the-art Ranking Technique Information Retrieval Experimental Result Ranking Result Ranking Model Much Attention |
| Content Type | Text |
| Resource Type | Article |
