Information Retrieval (IR) is the complex of activities that represent information as data and rank the data representing information relevant to the user's information needs by a retrieval function. Such a function involves parameters. They can in principle be set irrespective of the specific set of documents and queries, but can in practice maximize retrieval effectiveness. However, algorithms to select retrieval function parameters must be efficient due to the large search space. We can remark that: (i) all the tested methods are similarly effective, but the plots of the maximum value of NDCG@20 at a given evaluation show that our algorithm is more efficient; (ii) performance metrics and datasets studied in this paper seem to yield objective functions with few, if any, local optima with large basin of attraction; (iii) our algorithm is considerably more efficient, quickly finding parameterizations of the retrieval function yielding high performance - much faster than line search.

The retrieval function is one of the most important components of an Information Retrieval (IR) system, because it determines to what extent some information is relevant to a user query. Most retrieval functions have “free parameters” whose value must be set before retrieval, significantly affecting the effectiveness of an IR system. Choosing the optimum values for such parameters is therefore of paramount importance. However, the optimum can only be found after a computationally expensive process, especially when the generalization error is estimated via cross-validation. In this paper, we propose to determine free parameter values by solving an optimization problem aimed at maximizing a measure of retrieval effectiveness. We employ the black-box optimization paradigm, since the analytical expression of the measure of effectiveness with respect to the free parameters is unknown. We consider different methods for solving the black-box optimization problem: a simple grid-search over the whole domain, and more sophisticated techniques such as line search and surrogate model based algorithms. Experimental results on several test collections not only provide useful insight about effectiveness, but also about efficiency: they indicate that with appropriate optimization techniques, the computational cost of parameter optimization can be greatly reduced without compromising retrieval effectiveness, even when taking generalization into account.

Information retrieval (IR) systems are designed, in general, to satisfy the information need of a user who expresses it by means of a query, by providing him with a subset of documents selected from a collection and ordered by decreasing relevance to the query. Such systems are based on IR models, which define how to represent the documents and the query, as well as how to determine the relevance of a document for a query. In this article, we present a new IR model based on concepts taken from both IR and digital signal processing (like Fourier analysis of signals and filtering). This allows the whole IR process to be seen as a physical phenomenon, where the query corresponds to a signal, the documents correspond to filters, and the determination of the relevant documents to the query is done by filtering that signal. Tests showed that the quality of the results provided by this IR model is comparable with the state-of-the-art.