New query suggestion framework and algorithms: A case study for an educational search engine
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Query 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.
Açıklama
Anahtar Kelimeler
Query suggestion; Framework; Educational search engine; Query recommendation
Kaynak
Information Processing & Management
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
52
Sayı
5
