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.
Highlights
Ranking is an important component that directly affects the performances of information retrieval systems such as search engines, recommendation systems, and electronic commerce platforms
The main contributions of this work are summarized as follows: (1) We present a new function of the gradient in the LambdaMART algorithm that can highlight Matthew effect and prove that the function satisfies the consistency property
In order to measure the performances of the ranking model yielded by our improved method, we introduce the following utility metrics to characterize the Matthew effect of ranking models from different perspectives
Summary
Ranking is an important component that directly affects the performances of information retrieval systems such as search engines, recommendation systems, and electronic commerce platforms. This algorithm assigns a Web page with a higher score if the sum of its backlinks is high This algorithm considers the cast votes of pages. The PageRank algorithm exhibits the Matthew effect [4], which refers to the phenomenon that the rich get richer and the poor get poorer. This is valuable and reasonable in a search service, since people prefer to click on the links to some high-quality pages from other high-quality ones
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