基于线性回归的适应性排名算法研究

Adaptive ranking algorithm based on linear regression

根据已有的查询历史记录对排名模型进行自适应调整可以更好地实现检索结果的个性化。为了提高个性化检索的准确性,提出了一种基于线性回归的适应性排名算法。基于线性回归技术提出了一种适应性排名通用框架,该自适应框架通过调整参数来描述不同用户的查询偏好,进而实现排名的个性化,然后将改进的Rank SVM算法应用于该框架,并提出了一种适应性Rank SVM算法。最后,通过真实数据集实验验证了提出算法的有效性,能够明显提高排名准确率。

Adaptation of ranking model according to the query history could give a better personalized query results. In order to improve the precision of information retrieval, this paper proposed a linear regression based ranking algorithm. Firstly, it proposed an adaptive general ranking framework based on linear regression. Secondly, by introducing the RankSVM algorithm into the proposed framework, proposed an adaptive RankSVM algorithm. Finally, it validated the efficiency of the proposed al- gorithm using real dataset experiments.