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Combining prestige and relevance ranking for personalized recommendation

Authors:

Xiao Yang,

Zhaoxin ZhangAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1877 - 1880

https://doi.org/10.1145/2505515.2507885

Published: 27 October 2013 Publication History

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Abstract

In this paper, we present an adaptive graph-based personalized recommendation method based on combining prestige and relevance ranking. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships by analyzing the representation of user's preference in the graph. With different initialization and surfing strategies, this graph-based ranking model can take different type of data into account to capture personal interests from multiple perspectives. The experiments show that this algorithm can achieve better performance than the traditional CF methods and some graph-based recommendation methods.

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Cited By

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Sebri FDe Prado RBrahmi Z(2024)MVBA\_TSC: Majority Voting and Bayesian Average-Based Trustful Service Composition in Cloud and Edge Environments2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612121(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612121
Peng ZZhang HMin F(2024)IUG-CFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121887238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121887
Hamm AOdrowski S(2021)Term-Community-Based Topic Detection with Variable ResolutionInformation10.3390/info1206022112:6(221)Online publication date: 23-May-2021
https://doi.org/10.3390/info12060221
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Index Terms

Combining prestige and relevance ranking for personalized recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Sebri FDe Prado RBrahmi Z(2024)MVBA\_TSC: Majority Voting and Bayesian Average-Based Trustful Service Composition in Cloud and Edge Environments2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612121(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612121
Peng ZZhang HMin F(2024)IUG-CFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121887238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121887
Hamm AOdrowski S(2021)Term-Community-Based Topic Detection with Variable ResolutionInformation10.3390/info1206022112:6(221)Online publication date: 23-May-2021
https://doi.org/10.3390/info12060221
Qin J(2021)A Survey of Long-Tail Item Recommendation MethodsWireless Communications & Mobile Computing10.1155/2021/75363162021Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1155/2021/7536316
Heggli OKonvalinka IKringelbach MVuust P(2021)A metastable attractor model of self–other integration (MEAMSO) in rhythmic synchronizationPhilosophical Transactions of the Royal Society B: Biological Sciences10.1098/rstb.2020.0332376:1835Online publication date: 23-Aug-2021
https://doi.org/10.1098/rstb.2020.0332
Gong SCartlidge JBai RYue YLi QQiu G(2021)Geographical and temporal huff model calibration using taxi trajectory dataGeoinformatica10.1007/s10707-019-00390-x25:3(485-512)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s10707-019-00390-x
Ng YPera MPera SEkstrand MAmatriain XO'Donovan J(2018)Recommending social-interactive games for adults with autism spectrum disorders (ASD)Proceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240405(209-213)Online publication date: 27-Sep-2018
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Gong SCartlidge JBai RYue YLi QQiu G(2018)Automated prediction of shopping behaviours using taxi trajectory data and social media reviews2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367661(117-121)Online publication date: Mar-2018
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Badache IBoughanem MNordlie RPharo NFreund LLarsen BRussel D(2017)Fresh and Diverse Social SignalsProceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval10.1145/3020165.3020177(155-164)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3020165.3020177

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