Vidinli, I. BahattinOzcan, Rifat2025-10-242025-10-2420160306-45731873-5371https://doi.org/10.1016/j.ipm.2016.02.001https://hdl.handle.net/20.500.12899/3440Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as comparison of queries. We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method. (C) 2016 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessQuery suggestion; Framework; Educational search engine; Query recommendationArticle10.1016/j.ipm.2016.02.0015257337522-s2.0-84960984070Q1WOS:000381540600002Q1