extended-abstract

Towards the next generation of multi-criteria recommender systems

Author:

Zhe LiAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 553 - 557

https://doi.org/10.1145/3240323.3240326

Published: 27 September 2018 Publication History

Abstract

This paper presents the motivation, concepts, ideas and research questions underlying a PhD research project in the domain of recommender systems, and more specifically on multi-criteria recommendation. While we build on the existing work in this direction, we aim at introducing recommendation frameworks that do not only optimize for different criteria simultaneously, but also exploit their interrelations. For this aim, we will address three multi-criteria recommendation challenges, namely multi-modal user and item modeling, package recommendation, and user-centric recommendation. For realizing these frameworks, and in particular, for learning interactions and interrelations in the criteria space, we will rely on the state-of-the-art deep learning systems, and in particular the Generative Adversarial Networks (GANs). In addition, a novel evaluation strategy for multi-criteria recommendation targeting the maximization of the user's satisfaction will also be devised.

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Cited By

Sui JYin J(2021)A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis ModelApplied Sciences10.3390/app1112572411:12(5724)Online publication date: 20-Jun-2021
https://doi.org/10.3390/app11125724
Wasid MAli R(2021)A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2021.107782112(107782)Online publication date: Nov-2021
https://doi.org/10.1016/j.asoc.2021.107782

Index Terms

Towards the next generation of multi-criteria recommender systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 Owner/Author.

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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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RecSys '18

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SIGCHI

RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

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

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RecSys '24

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18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Cited By

Sui JYin J(2021)A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis ModelApplied Sciences10.3390/app1112572411:12(5724)Online publication date: 20-Jun-2021
https://doi.org/10.3390/app11125724
Wasid MAli R(2021)A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2021.107782112(107782)Online publication date: Nov-2021
https://doi.org/10.1016/j.asoc.2021.107782

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