Selective Query Processing: a Risk-Sensitive Selection of System Configurations

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the system configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an approach, in which the system decides automatically whether or not to expand the query, resulting in two possible system configurations. This approach was extended recently to include many other parameters, leading to many possible system configurations where the system automatically selects the best configuration on a per-query basis. To determine the ideal configurations to use on a per-query basis in real-world systems we developed a method in which a restricted number of possible configurations is pre-selected and then used in a meta-search engine that decides the best search configuration on a per query basis. We define a risk-sensitive approach for configuration pre-selection that considers the risk-reward trade-off between the number of configurations kept, and system effectiveness. For final configuration selection, the decision is based on query feature similarities. We find that a relatively small number of configurations (20) selected by our risk-sensitive model is sufficient to increase effectiveness by about 15% according(P@10, nDCG@10) when compared to traditional grid search using a single configuration and by about 20% when compared to learning to rank documents. Our risk-sensitive approach works for both diversity- and ad hoc-oriented searches. Moreover, the similarity-based selection method outperforms the more sophisticated approaches. Thus, we demonstrate the feasibility of developing per-query information retrieval systems, which will guide future research in this direction.Comment: 30 pages, 5 figures, 8 tables; submitted to TOIS ACM journa

Learning to Rank System Configurations

International audienceInformation Retrieval (IR) systems heavily rely on a large number of parameters, such as the retrieval model or various query expansion parameters, whose values greatly in uence the overall retrieval effectiveness. However, setting all these parameters individually can often be a tedious task, since they can all affect one another, while also vary for different queries. We propose to tackle this problem by dealing with entire system configurations (i.e. a set of parameters representing an IR system) instead of single parameters, and to apply state-of-the-art Learning to Rank techniques to select the most appropriate configuration for a given query. The experiments we conducted on two TREC AdHoc collections show that this approach is feasible and significantly outperforms the traditional way to configure a system using grid search, as well as the top performing systems of the TREC tracks. We also show an analysis on the impact of different groups of parameters on retrieval effectiveness