Editing Apache Lucene (section)

==Features and common use==
While suitable for any application that requires full text [[Index (search engine)|indexing]] and searching capability, Lucene is recognized for its utility in the implementation of [[Internet search engine]]s and local, single-site searching.<ref>{{cite book|url=https://archive.org/details/luceneactionseco00hatc|url-access=limited|title=Lucene in Action, Second Edition|last1=McCandless|first1=Michael|last2=Hatcher|first2=Erik|last3=Gospodnetić|first3=Otis|publisher=Manning|year=2010|isbn=978-1933988177|page=[https://archive.org/details/luceneactionseco00hatc/page/n46 8]}}</ref><ref>{{cite web|url=http://www.glscube.org/downloads/glscube_design.pdf|title=GNU/Linux Semantic Storage System|website=glscube.org|archive-url=https://web.archive.org/web/20100601210729/http://www.glscube.org/downloads/glscube_design.pdf|archive-date=2010-06-01|url-status=dead}}</ref>

Lucene includes a feature to perform a fuzzy search based on [[Levenshtein distance|edit distance]].<ref>{{cite web|url=https://lucene.apache.org/core/2_9_4/queryparsersyntax.html#Fuzzy+Searches|title=Apache Lucene - Query Parser Syntax|website=lucene.apache.org|url-status=live|archive-url=https://web.archive.org/web/20170502011748/http://lucene.apache.org/core/2_9_4/queryparsersyntax.html#Fuzzy+Searches|archive-date=2017-05-02}}</ref>

Lucene has also been used to implement recommendation systems.<ref>J. Beel, S. Langer, and B. Gipp, “The Architecture and Datasets of Docear’s Research Paper Recommender System,” in Proceedings of the 3rd International Workshop on Mining Scientific Publications (WOSP 2014) at the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014), London, UK, 2014</ref> For example, Lucene's 'MoreLikeThis' Class can generate recommendations for similar documents. In a comparison of the term vector-based similarity approach of 'MoreLikeThis' with citation-based document similarity measures, such as [[co-citation]] and co-citation proximity analysis, Lucene's approach excelled at recommending documents with very similar structural characteristics and more narrow relatedness.<ref name="Schwarzer16">M. Schwarzer, M. Schubotz, N. Meuschke, C. Breitinger, [[Volker Markl|V. Markl]], and B. Gipp, https://www.gipp.com/wp-content/papercite-data/pdf/schwarzer2016.pdf "Evaluating Link-based Recommendations for Wikipedia" in Proceedings of the 16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), New York, NY, USA, 2016, pp. 191-200.</ref> In contrast, citation-based document similarity measures tended to be more suitable for recommending more broadly related documents,<ref name="Schwarzer16" /> meaning citation-based approaches may be more suitable for generating [[Recommender system#Beyond accuracy|serendipitous]] recommendations, as long as documents to be recommended contain in-text citations.