基于矩阵分解的伪相关反馈技术

摘要

伪相关反馈技术的性能很大程度上依赖2个参数的取值，在缺乏结果相关性评价的前提下，这些参数只能依靠经验设置。文中提出基于矩阵分的伪相关反馈技术。该技术将多个伪相关反馈结果使用协同过滤的思想融合，自动选择最优化参数进查询扩展。实验表明，与现有的伪相关反馈技术相比，无论使用哪种信息检索模型，文中方法的检索性能都能得到较好改善。%The performance of pseudo﹣relevance feedback technique is heavily dependent on two parameter values. Under the lack of relevance valuation results, these parameters can only rely on experience to set. In this paper, a pseudo﹣relevance feedback technique based on matrix factorization is proposed. This technique fuses multiple pseudo﹣relevance feedback results using the ideas of collaborative filtering together. And the optimal parameters are automatically selected for query expansion. Experimental results show that compared with the existing pseudo﹣relevance feedback techniques, the proposed method has a better retrieval performance, regardless of any underlying information retrieval model.