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A survey of recommendation techniques based on offline data processing

Authors:

Wanlei ZhouAuthors Info & Claims

Concurrency and Computation: Practice & Experience, Volume 27, Issue 15

Pages 3915 - 3942

https://doi.org/10.1002/cpe.3370

Published: 01 October 2015 Publication History

Abstract

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. Copyright © 2014 John Wiley & Sons, Ltd.

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Cited By

Binst B(2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688017
Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555161
Kaur MSalim FRen YChan JTomko MSanderson M(2020)Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor BehaviorsACM Transactions on Sensor Networks10.1145/339369216:3(1-25)Online publication date: 13-Aug-2020
https://dl.acm.org/doi/10.1145/3393692
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Published In

cover image Concurrency and Computation: Practice & Experience

Concurrency and Computation: Practice & Experience Volume 27, Issue 15

October 2015

282 pages

ISSN:1532-0626

EISSN:1532-0634

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 01 October 2015

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Cited By

Binst B(2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688017
Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555161
Kaur MSalim FRen YChan JTomko MSanderson M(2020)Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor BehaviorsACM Transactions on Sensor Networks10.1145/339369216:3(1-25)Online publication date: 13-Aug-2020
https://dl.acm.org/doi/10.1145/3393692
Kaur MSalim FRen YChan JTomko MSanderson MGupta RHuang PGonzalez M(2018)Shopping intent recognition and location prediction from cyber-physical activities via wi-fi logsProceedings of the 5th Conference on Systems for Built Environments10.1145/3276774.3276786(130-139)Online publication date: 7-Nov-2018
https://dl.acm.org/doi/10.1145/3276774.3276786
Sharif NAfzal MChbeir RManolopoulos YMammana VModena ETraina ASalviano OBadr YAndres F(2015)Recommendation approaches for e-learnersProceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems10.1145/2857218.2857251(137-141)Online publication date: 25-Oct-2015
https://dl.acm.org/doi/10.1145/2857218.2857251

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