research-article

Mobile recommendation challenges within a strong privacy oriented paradigm

Authors:

Frederic RoullandAuthors Info & Claims

LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

Article No.: 7, Pages 1 - 9

https://doi.org/10.1145/3356994.3365506

Published: 05 November 2019 Publication History

Abstract

A recent shift towards more user data privacy supported in that sense by recent regulations may deeply impact the way search and recommendation systems work. We explore in this paper some constraints and challenges raised by such a paradigm shift in a live experiment aiming at developing an ambient and context aware personal recommender system for mobile users in a strong privacy by-design environment. We discuss the impact of a lack of precision for stay area detection and focus more specifically on semantic disambiguation challenges for point of interest inference to model valuable user profiles. We present and discuss our findings from a live experiment made in the cities of Lyon and Grenoble in France.

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Cited By

Ferreira GAlves AVeloso MBento C(2022)Identification and Classification of Routine Locations Using Anonymized Mobile Communication DataISPRS International Journal of Geo-Information10.3390/ijgi1104022811:4(228)Online publication date: 29-Mar-2022
https://doi.org/10.3390/ijgi11040228
Bouros PDasu TKanza YRenz MSacharidis D(2020)LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3383653.338366511:3(30-33)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383665

Index Terms

Mobile recommendation challenges within a strong privacy oriented paradigm
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

November 2019

92 pages

ISBN:9781450369633

DOI:10.1145/3356994

Editors:
Panagiotis Bouros
Johannes Gutenberg University Mainz, Germany
,
Tamraparni Dasu
AT&T Labs - Research
,
Yaron Kanza
AT&T Labs - Research
,
Matthias Renz
Christian-Albrechts-Universität zu Kiel, Germany
,
Dimitris Sacharidis
Technische Universität Wien, Austria

Copyright © 2019 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2019

Illinois, Chicago

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LocalRec '19 Paper Acceptance Rate 6 of 12 submissions, 50%;

Overall Acceptance Rate 17 of 26 submissions, 65%

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Cited By

Ferreira GAlves AVeloso MBento C(2022)Identification and Classification of Routine Locations Using Anonymized Mobile Communication DataISPRS International Journal of Geo-Information10.3390/ijgi1104022811:4(228)Online publication date: 29-Mar-2022
https://doi.org/10.3390/ijgi11040228
Bouros PDasu TKanza YRenz MSacharidis D(2020)LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3383653.338366511:3(30-33)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383665

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