demonstration

RecLand: A Recommender System for Social Networks

Authors:

Ryadh Dahimene,

Camelia Constantin,

Cédric du MouzaAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 2063 - 2065

https://doi.org/10.1145/2661829.2661850

Published: 03 November 2014 Publication History

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Abstract

Social networks have become an important information source. Due to their unprecedented success, these systems have to face an exponentially increasing amount of user generated content. As a consequence, finding relevant users or data matching specific interests is a challenging. We present RecLand, a recommender system that takes advantage of the social graph topology and of the existing contextual information to recommend users. The graphical interface of RecLand shows recommendations that match the topical interests of users and allows to tune the parameters to adapt the recommendations to their needs.

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Cited By

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Bertini MFerracani APapucci RDel Bimbo A(2020)Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual ContentAdjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3386392.3397594(29-34)Online publication date: 14-Jul-2020
https://dl.acm.org/doi/10.1145/3386392.3397594
Sundararaman DSeshagiri VRamesh BArora P(2018)YouTreeProceedings of the 10th International Conference on Computer Modeling and Simulation10.1145/3177457.3177467(123-128)Online publication date: 8-Jan-2018
https://dl.acm.org/doi/10.1145/3177457.3177467
Pozo MChiky RMeziane FMetais E(2016)An item/user representation for recommender systems based on bloom filters2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2016.7549311(1-12)Online publication date: Jun-2016
https://doi.org/10.1109/RCIS.2016.7549311
Show More Cited By

Index Terms

RecLand: A Recommender System for Social Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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Published In

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 03 November 2014

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CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

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Overall Acceptance Rate 1,466 of 6,316 submissions, 23%

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Bertini MFerracani APapucci RDel Bimbo A(2020)Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual ContentAdjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3386392.3397594(29-34)Online publication date: 14-Jul-2020
https://dl.acm.org/doi/10.1145/3386392.3397594
Sundararaman DSeshagiri VRamesh BArora P(2018)YouTreeProceedings of the 10th International Conference on Computer Modeling and Simulation10.1145/3177457.3177467(123-128)Online publication date: 8-Jan-2018
https://dl.acm.org/doi/10.1145/3177457.3177467
Pozo MChiky RMeziane FMetais E(2016)An item/user representation for recommender systems based on bloom filters2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2016.7549311(1-12)Online publication date: Jun-2016
https://doi.org/10.1109/RCIS.2016.7549311
Dougnon RFournier-Viger PLin JNkambou R(2016)Inferring social network user profiles using a partial social graphJournal of Intelligent Information Systems10.1007/s10844-016-0402-y47:2(313-344)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s10844-016-0402-y
Li YConstantin CMouza C(2016)A Block-Based Edge Partitioning for Random Walks Algorithms over Large Social GraphsProceedings of the 17th International Conference on Web Information Systems Engineering - Volume 1004210.1007/978-3-319-48743-4_22(275-289)Online publication date: 7-Nov-2016
https://dl.acm.org/doi/10.1007/978-3-319-48743-4_22
Moussa S(2016)An Approach for Opinion-Demographic-Topology Based Microblog Friend RecommendationProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201610.1007/978-3-319-48308-5_78(815-824)Online publication date: 18-Oct-2016
https://doi.org/10.1007/978-3-319-48308-5_78

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Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items

Using a trust network to improve top-N recommendation

User Similarity Adjustment for Improved Recommendations

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