Efficient parameter estimation for information retrieval using black-box optimization (extended abstract)

Abstract

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.