An Improved LambdaMART Algorithm Based on the Matthew Effect

Abstract

Matthew effect is a desirable phenomenon for a ranking model in search engines and recommendation systems. However, most of algorithms of learning to rank (LTR) do not pay attention to Matthew effect. LambdaMART is a well-known LTR algorithm that can be further optimized based on Matthew effect. Inspired by Matthew effect, we distinguish queries with different effectiveness and then assign a higher weight to a query with higher effectiveness. We improve the gradient in the LambdaMART algorithm to optimize the queries with high effectiveness, that is, to highlight the Matthew effect of the produced ranking models. In addition, we propose strategies of evaluating a ranking model and dynamically decreasing the learning rate to both strengthen the Matthew effect of ranking models and improve the effectiveness of ranking models. We use Gini coefficient, mean-variance, quantity statistics, and winning number to measure the performances of the ranking models. Experimental results on multiple benchmark datasets show that the ranking models produced by our improved LambdaMART algorithm can exhibit a stronger Matthew effect and achieve higher effectiveness compared to the original one and other state-of-the-art LTR algorithms.