  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.
