Learning to Rank with Likelihood Loss Functions

A novel machine learning framework for ranking, which depends on two new samples, and a novel preference-weighted loss function for multiple relevance judgment data sets, which are effective in improving ranking performance.

Abstract

According to a given query in training set, the documents can be grouped based on their relevance judgments. If the group with higher relevance labels is in front of the one with lower relevance judgments, the ranking performance of ranking model could be perfect. Inspired by this idea, we propose a novel machine learning framework for ranking, which depends on two new samples. The first sample is one-group constructed of one document with higher relevance judgment and a group of documents with lower relevance judgment; the second sample is group-group constructed of a group of documents with higher relevance judgment and a group of documents with lower relevance judgment. We also develop a novel preference-weighted loss function for multiple relevance judgment data sets. Finally, we optimize the group ranking approaches by optimizing initial ranking list for likelihood loss function. Experimental results show that our approaches are effective in improving ranking performance.