A survey of recommendation techniques based on offline data processing
Yongli Ren, Gang Li, Wanlei Zhou · Concurrency and Computation Practice and Experience · 2014
Summary 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 astemporal recommendation,graph‐based recommendationandtrust‐based recommendation), new features (such asserendipitous recommendation) and new research issues (such astag recommendationandgroup recommendation). 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 & Sons, Ltd.