New Application of Life Rank Algorithm: A Case Study

The past few years have seen the emergence of learning-to-rank (LTR) in the field of machine learning. In information acquiring the size of data is very large and empowering a learning-to-rank model on it will be a costly and time taking process. High dimension data leads to irrelevant and redundant data which results in overfitting. “Dimensionality reduction” methods are used to manage this issue. There are two-dimensionality reduction techniques namely feature selection and feature reduction. There is extensive research available on the algorithm for learning-to-rank but this not the case for dimensionality reduction approaches in LTR, despite its importance. Feature selection techniques for classification are directly used for ranking. To the best of our understanding, feature extraction techniques in the context of ranking problems are not explored much to date. So, we make an effort to fill this void and explore feature extraction in the context of LTR problems. The LifeRank algorithm is a linear feature extraction algorithm for ranking. Its performance is analyzed on RankSVM and Linear regression. It is not applied to other learning-to-rank algorithms. So, in this task, an attempt is made to study the effect of the application of the LifeRank algorithm on other LTR algorithms. LifeRank algorithm is applied on RankNet and RankBoost. Then, the performance of several LTR algorithms on the LETOR dataset is analyzed before and after feature extraction.