학술논문

Multiobjective evolutionary algorithms for context‐based search
Document Type
redif-article
Source
Association for Information Science & Technology, Journal of the American Society for Information Science and Technology. 61(6):1258-1274
Subject
Language
English
Abstract
Formulating high‐quality queries is a key aspect of context‐based search. However, determining the effectiveness of a query is challenging because multiple objectives, such as high precision and high recall, are usually involved. In this work, we study techniques that can be applied to evolve contextualized queries when the criteria for determining query quality are based on multiple objectives. We report on the results of three different strategies for evolving queries: (a) single‐objective, (b) multiobjective with Pareto‐based ranking, and (c) multiobjective with aggregative ranking. After a comprehensive evaluation with a large set of topics, we discuss the limitations of the single‐objective approach and observe that both the Pareto‐based and aggregative strategies are highly effective for evolving topical queries. In particular, our experiments lead us to conclude that the multiobjective techniques are superior to a baseline as well as to well‐known and ad hoc query reformulation techniques.