Deakin University
Browse

File(s) under permanent embargo

A survey of recommendation techniques based on offline data processing

journal contribution
posted on 2015-10-01, 00:00 authored by Yongli Ren, Gang LiGang Li, Wanlei Zhou
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 temporal recommendation, graph-based recommendation and trust-based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group 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.

History

Journal

Concurrency computation: practice and experience

Volume

27

Issue

15

Pagination

3915 - 3942

Publisher

Wiley

Location

London, Eng.

ISSN

1532-0626

eISSN

1532-0634

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2015, Wiley