中文摘要：

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解的伪相关反馈技术．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行查询扩展．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．

英文摘要：

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.