A survey of recommendation techniques based on offline data processing. (9th September 2014)

• Record Type: Journal Article
• Authors: Ren, Yongli; Li, Gang; Zhou, Wanlei; Orgun, Mehmet A.; Orgun, Atilla Elçi; Pieprzyk, Josef; Chefranov, Alexander; Shankaran, Rajan; Wang, Huaxiong; Zhuge, Hai
• Is Part Of: Concurrency and computation. Volume 27:Number 15(2015:Oct.)
• Page Start: 3915
• Page End: 3942
• Publication Date: 2014-09-09
• Subjects: Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3370
• Languages: English
• ISSNs: 1532-0626

Abstract

<abstract abstract-type="main" id="cpe3370-abs-0001"> <title>Summary</title> <p id="cpe3370-para-0001">Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real‐world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as <italic>temporal recommendation</italic>, <italic>graph‐based recommendation</italic> and <italic>trust‐based recommendation</italic>), new features (such as <italic>serendipitous recommendation</italic>) and new research issues (such as <italic>tag recommendation</italic> and <italic>group recommendation</italic>). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>