research-article

Inferring Contextual Preferences Using Deep Auto-Encoding

Authors:

Bracha Shapira,

Ariel BarAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 221 - 229

https://doi.org/10.1145/3079628.3079666

Published: 09 July 2017 Publication History

Abstract

Context-aware systems enable the sensing and analysis of user context in order to provide personalized services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilized to infer users' dynamic preferences. We present a novel method for inferring contextual user preferences by using an unsupervised deep learning technique applied to mobile sensor data. We train an auto-encoder for each user preference with contextual data that based on past user interaction with the system. Given new contextual sensor data from a user, the patterns discovered from each auto-encoder are used to predict the most likely preference in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual preferences based on the interaction of users with categories of items. Empirical results utilizing a real world dataset of mobile users show a significant improvement (16% to 73% improvement) in classification accuracy compared with state of the art classification methods.

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Cited By

An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
https://doi.org/10.62762/TSCC.2024.898503
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
Mateos PBellogín A(2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
https://doi.org/10.1007/s10462-024-10939-4
Show More Cited By

Index Terms

Inferring Contextual Preferences Using Deep Auto-Encoding
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

Copyright © 2017 ACM.

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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Published: 09 July 2017

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

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Cited By

An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
https://doi.org/10.62762/TSCC.2024.898503
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
Mateos PBellogín A(2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
https://doi.org/10.1007/s10462-024-10939-4
Zheng Y(2022)Context-Aware Collaborative Filtering Using Context Similarity: An Empirical ComparisonInformation10.3390/info1301004213:1(42)Online publication date: 17-Jan-2022
https://doi.org/10.3390/info13010042
Guo SShi YXiao PFu YLin JZeng WLee T(2022)Creative and Progressive Interior Color Design with Eye-tracked User PreferenceACM Transactions on Computer-Human Interaction10.1145/354292230:1(1-31)Online publication date: 8-Jun-2022
https://dl.acm.org/doi/10.1145/3542922
Zheng YBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)DeepCARSKit: A Demo and User GuideAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536417(18-21)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3536417
Zheng YFlorez Arias GBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)A Family of Neural Contextual Matrix Factorization Models for Context-Aware RecommendationsAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536404(1-6)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3536404
Unger MTuzhilin ALivne A(2020)Context-Aware Recommendations Based on Deep Learning FrameworksACM Transactions on Management Information Systems10.1145/338624311:2(1-15)Online publication date: 22-May-2020
https://dl.acm.org/doi/10.1145/3386243
Kristoffersen MShepstone STan Z(2020)The Importance of Context When Recommending TV Content: Dataset and AlgorithmsIEEE Transactions on Multimedia10.1109/TMM.2019.294421422:6(1531-1541)Online publication date: Jun-2020
https://doi.org/10.1109/TMM.2019.2944214
Unger MTuzhilin A(2020)Hierarchical Latent Context Representation for Context-Aware RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3022102(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2020.3022102
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