A Simple Linear Ranking Algorithm Using Query Dependent Intercept Variables

TL;DR

This paper introduces a simple linear ranking model with query-dependent intercept variables, improving ranking performance on benchmark IR datasets by accounting for query-specific relevance biases.

Contribution

The novel contribution is the incorporation of query-specific intercept variables into a linear ranking model, which enhances ranking accuracy over existing algorithms.

Findings

Outperforms participating algorithms on benchmark datasets

Uses standard logistic regression with added query intercepts

Simple approach with promising results

Abstract

The LETOR website contains three information retrieval datasets used as a benchmark for testing machine learning ideas for ranking. Algorithms participating in the challenge are required to assign score values to search results for a collection of queries, and are measured using standard IR ranking measures (NDCG, precision, MAP) that depend only the relative score-induced order of the results. Similarly to many of the ideas proposed in the participating algorithms, we train a linear classifier. In contrast with other participating algorithms, we define an additional free variable (intercept, or benchmark) for each query. This allows expressing the fact that results for different queries are incomparable for the purpose of determining relevance. The cost of this idea is the addition of relatively few nuisance parameters. Our approach is simple, and we used a standard logistic regression library to test it. The results beat the reported participating algorithms. Hence, it seems promising to combine our approach with other more complex ideas.