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Introduction to the Special Issue on Diversity and Discovery in Recommender Systems

Authors:

Pablo Castells,

Jun Wang,

Rubén Lara,

Dell ZhangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 4

Article No.: 52, Pages 1 - 3

https://doi.org/10.1145/2668113

Published: 15 December 2014 Publication History

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Cited By

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Qassimi SAbdelwahed EHafidi MQazdar A(2021)Towards a folksonomy graph-based context-aware recommender system of annotated booksJournal of Big Data10.1186/s40537-021-00457-38:1Online publication date: 13-May-2021
https://doi.org/10.1186/s40537-021-00457-3
He WAi DWu C(2021)A recommender model based on strong and weak social TiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115483184:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115483
Zhang HSun YZhao MChow TWu Q(2020)Bridging User Interest to Item Content for Recommender Systems: An Optimization ModelIEEE Transactions on Cybernetics10.1109/TCYB.2019.290015950:10(4268-4280)Online publication date: Oct-2020
https://doi.org/10.1109/TCYB.2019.2900159
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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 4

Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

January 2015

390 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2699158

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 December 2014

Published in TIST Volume 5, Issue 4

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Qassimi SAbdelwahed EHafidi MQazdar A(2021)Towards a folksonomy graph-based context-aware recommender system of annotated booksJournal of Big Data10.1186/s40537-021-00457-38:1Online publication date: 13-May-2021
https://doi.org/10.1186/s40537-021-00457-3
He WAi DWu C(2021)A recommender model based on strong and weak social TiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115483184:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115483
Zhang HSun YZhao MChow TWu Q(2020)Bridging User Interest to Item Content for Recommender Systems: An Optimization ModelIEEE Transactions on Cybernetics10.1109/TCYB.2019.290015950:10(4268-4280)Online publication date: Oct-2020
https://doi.org/10.1109/TCYB.2019.2900159
Jannach DResnick PTuzhilin AZanker M(2016)Recommender systems — beyond matrix completionCommunications of the ACM10.1145/289140659:11(94-102)Online publication date: 28-Oct-2016
https://dl.acm.org/doi/10.1145/2891406

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Special Issue on Discovery Science

A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems

Temporal diversity in recommender systems

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