research-article

Personalized collaborative clustering

Authors:

Khalid El-Arini,

Carlos GuestrinAuthors Info & Claims

WWW '14: Proceedings of the 23rd international conference on World wide web

Pages 75 - 84

https://doi.org/10.1145/2566486.2567991

Published: 07 April 2014 Publication History

Abstract

We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.

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Cited By

Le DLauw HSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Collaborative Curating for Discovery and Expansion of Visual ClustersProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498504(544-552)Online publication date: 15-Feb-2022
https://doi.org/10.1145/3488560.3498504
Dereventsov AVatsavai RWebster C(2021)On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00097(742-749)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00097
Ghosh NChen YYue YWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Landmark ordinal embeddingProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455319(11506-11515)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455319
Show More Cited By

Index Terms

Personalized collaborative clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
  2. Interaction design
    1. Interaction design theory, concepts and paradigms

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '14: Proceedings of the 23rd international conference on World wide web

April 2014

926 pages

ISBN:9781450327442

DOI:10.1145/2566486

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Office of Naval Research

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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WWW '14 Paper Acceptance Rate 84 of 645 submissions, 13%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Le DLauw HSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Collaborative Curating for Discovery and Expansion of Visual ClustersProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498504(544-552)Online publication date: 15-Feb-2022
https://doi.org/10.1145/3488560.3498504
Dereventsov AVatsavai RWebster C(2021)On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00097(742-749)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00097
Ghosh NChen YYue YWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Landmark ordinal embeddingProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455319(11506-11515)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455319
Le DLauw H(2019)Learning multiple maps from conditional ordinal tripletsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367430(2815-2822)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367430
Le DLauw HCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Multiperspective Graph-Theoretic Similarity MeasureProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271758(1223-1232)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271758
Deng ZNavarathna RCarr PMandt SYue YMatthews IMori G(2017)Factorized Variational Autoencoders for Modeling Audience Reactions to Movies2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2017.637(6014-6023)Online publication date: Jul-2017
https://doi.org/10.1109/CVPR.2017.637
Cheng TLin KGong XLiu KWu S(2016)Learning user perceived clusters with feature-level supervisionProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157096.3157156(532-540)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.5555/3157096.3157156
Chan JDang SDow SKaye JDruin ALampe CMorris DHourcade J(2016)Comparing Different Sensemaking Approaches for Large-Scale IdeationProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858178(2717-2728)Online publication date: 7-May-2016
https://dl.acm.org/doi/10.1145/2858036.2858178
Li ZLi HShao L(2016)Improving Online Customer Shopping Experience with Computer Vision and Machine Learning MethodsHCI in Business, Government, and Organizations: eCommerce and Innovation10.1007/978-3-319-39396-4_39(427-436)Online publication date: 22-Jun-2016
https://doi.org/10.1007/978-3-319-39396-4_39
Zhao TMcAuley JKing IBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Improving Latent Factor Models via Personalized Feature Projection for One Class RecommendationProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806511(821-830)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806511
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