poster

Modeling recommendation as a social choice problem

Authors:

Massimiliano Albanese,

Antonio d'Acierno,

Vincenzo Moscato,

Fabio Persia,

Antonio PicarielloAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 329 - 332

https://doi.org/10.1145/1864708.1864779

Published: 26 September 2010 Publication History

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Abstract

In the classical theory of social choice, a set of voters is called to rank a set of alternatives and a social ranking of the alternatives is generated. In this paper, we model recommendation in the context of browsing systems as a social choice problem, where the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We then propose an importance ranking method that strongly resembles the well known PageRank ranking system, and takes into account both the browsing behavior of the users and the intrinsic features of the objects in the collection. We apply the proposed approach in the context of multimedia browsing systems and show that it can generate effective recommendations and can scale well for large data collections.

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Cited By

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Nia RJalali M(2022)RecMemComputational Intelligence and Neuroscience10.1155/2022/87148702022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8714870
Sperlì GAmato FMercorio FMezzanzanica MMoscato VPicariello A(2021)A Social Media Recommender SystemResearch Anthology on Strategies for Using Social Media as a Service and Tool in Business10.4018/978-1-7998-9020-1.ch028(541-556)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9020-1.ch028
Shahbazi ZHazra DPark SByun Y(2020)Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding ApproachesSymmetry10.3390/sym1209156612:9(1566)Online publication date: 22-Sep-2020
https://doi.org/10.3390/sym12091566
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Index Terms

Modeling recommendation as a social choice problem
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information systems applications
    1. Digital libraries and archives
    2. Multimedia information systems

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Information & Contributors

Information

Published In

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

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: 26 September 2010

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RecSys '10

Sponsor:

SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all

Nia RJalali M(2022)RecMemComputational Intelligence and Neuroscience10.1155/2022/87148702022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8714870
Sperlì GAmato FMercorio FMezzanzanica MMoscato VPicariello A(2021)A Social Media Recommender SystemResearch Anthology on Strategies for Using Social Media as a Service and Tool in Business10.4018/978-1-7998-9020-1.ch028(541-556)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9020-1.ch028
Shahbazi ZHazra DPark SByun Y(2020)Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding ApproachesSymmetry10.3390/sym1209156612:9(1566)Online publication date: 22-Sep-2020
https://doi.org/10.3390/sym12091566
Zhang ZKudo YMurai TRen Y(2019)Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance WeightingApplied Sciences10.3390/app90919289:9(1928)Online publication date: 10-May-2019
https://doi.org/10.3390/app9091928
Amato ABalzano WCozzolino GMoscato F(2019)Analysis of Consumers Perceptions of Food Safety Risk in Social NetworksAdvanced Information Networking and Applications10.1007/978-3-030-15032-7_102(1217-1227)Online publication date: 15-Mar-2019
https://doi.org/10.1007/978-3-030-15032-7_102
Amato ACozzolino G(2019)Trust Analysis for Information Concerning Food-Related RisksAdvances in Internet, Data and Web Technologies10.1007/978-3-030-12839-5_31(344-354)Online publication date: 6-Feb-2019
https://doi.org/10.1007/978-3-030-12839-5_31
Sperlì GAmato FMercorio FMezzanzanica MMoscato VPicariello A(2018)A Social Media Recommender SystemInternational Journal of Multimedia Data Engineering & Management10.4018/IJMDEM.20180101039:1(36-50)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJMDEM.2018010103
Amato FBarolli LMoscato VPicariello ASperli G(2018)Strategies for Social Networks Modeling2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)10.1109/WAINA.2018.00167(681-686)Online publication date: May-2018
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Amato FMoscato VPicariello APiccialli F(2017)SOS: A multimedia recommender System for Online Social networksFuture Generation Computer Systems10.1016/j.future.2017.04.028Online publication date: Apr-2017
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