On the effectiveness of query weighting for adapting rank learners to new unlabelled collections

Query-level instance weighting is a technique for unsupervised transfer ranking, which aims to train a ranker on a source collection so that it also performs effectively on a target collection, even if no judgement information exists for the latter. Past work has shown that this approach can be used to significantly improve effectiveness; in this work, the approach is re-examined on a wide set of publicly available L2R test collections with more advanced learning to rank algorithms. Different query-level weighting strategies are examined against two transfer ranking frameworks: AdaRank and a new weighted LambdaMART algorithm. Our experimental results show that the effectiveness of different weighting strategies, including those shown in past work, vary under different transferring environments. In particular, (i) Kullback-Leibler based density-ratio estimation tends to outperform a classification-based approach and (ii) aggregating document-level weights into query-level weights is likely superior to direct estimation using a query-level representation. The Nemenyi statistical test, applied across multiple datasets, indicates that most weighting transfer learning methods do not significantly outperform baselines, although there is potential for the further development of such techniques.