Improving Learning to Rank By Leveraging User Dynamics and Continuation Methods

Learning to Rank (LtR) techniques leverage assessed sam-
ples of query-document relevance to learn ranking functions able to ex-
ploit the noisy signals hidden in the features used to represent queries
and documents. In this paper, we explore how to enhance the state-of-
the-art LambdaMart algorithm by integrating in the training process
an explicit knowledge of the underlying user-interaction model and the
possibility of targeting different objective functions, that can effectively
drive the algorithm towards promising areas of the search space. We
enrich the learning algorithm in two ways: (i) by considering complex
query-based user dynamics instead than simply discounting the gain by
the rank position; (ii) by designing a learning path across different loss
functions that can capture different signals in the training data. Our
extensive experiments, conducted on publicly available datasets, show
that the proposed solution permits to improve various ranking quality
measures by statistically significant margins.