基于检索结果排序的伪相关反馈

针对传统伪相关反馈（PRF）算法扩展源质量不高使得检索效果不佳的问题，提出一种基于检索结果的排序模型（REM）。首先，该模型从初检结果中选择排名靠前的文档作为伪相关文档集；然后，以用户查询意图与伪相关文档集中各文档的相关度最大化、并且各文档之间相似性最小化作为排序原则，将伪相关文档集中各文档进行重排序；最后，将排序后排名靠前的文档作为扩展源进行二次反馈。实验结果表明，与两种传统伪反馈方法相比，该排序模型能获得与用户查询意图相关的反馈文档，可有效地提高检索效果。

Pseudo relevance feedback based on sorted retrieval result

Focusing on the low quality of expansion source of traditional Pseudo Relevance Feedback (PRF) algorithms, which lead to low retrieval performance, a retrieval result based sorting model, namely REM, was proposed. Firstly, the first-pass retrieval result was considered as a pseudo relevant set. Secondly, documents in the pseudo relevant set were re-ranked based on rules of maximizing the relevance between the user query intention and the documents of pseudo relevant set and minimizing the similarity between documents. Finally, the top ranked documents of the re-ranking were regarded as the expansion source to the second-retrieval. The experimental results show that, compared with two classical PRF methods, the proposed model can improve the performance of retrieval and obtain more relevant feedback document to the user query intention.