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short-paper

POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences

Authors:

Jean-Benoit Griesner,

Talel Abdessalem,

Hubert NaackeAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 301 - 304

https://doi.org/10.1145/2792838.2799679

Published: 16 September 2015 Publication History

Abstract

Providing personalized point-of-interest (POI) recommendation has become a major issue with the rapid emergence of location-based social networks (LBSNs). Unlike traditional recommendation approaches, the LBSNs application domain comes with significant geographical and temporal dimensions. Moreover most of traditional recommendation algorithms fail to cope with the specific challenges implied by these two dimensions. Fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains unexplored, as far as we know. We depict how matrix factorization can serve POI recommendation, and propose a novel attempt to integrate both geographical and temporal influences into matrix factorization. Specifically we present GeoMF-TD, an extension of geographical matrix factorization with temporal dependencies. Our experiments on a real dataset shows up to 20\% benefit on recommendation precision.

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Cited By

Rahmani HAliannejadi MBaratchi MCrestani F(2022)A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest RecommendationACM Transactions on Information Systems10.1145/350847840:4(1-35)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3508478
Chakraborty AGanguly DCaputo AJones G(2022)Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendationInformation Retrieval10.1007/s10791-021-09400-925:1(44-90)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10791-021-09400-9
Liu YSong ZXu XRafique WZhang XShen JKhosravi MQi L(2022)Bidirectional GRU networks‐based next POI category prediction for healthcareInternational Journal of Intelligent Systems10.1002/int.2271037:7(4020-4040)Online publication date: 26-May-2022
https://dl.acm.org/doi/10.1002/int.22710
Show More Cited By

Index Terms

POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

Copyright © 2015 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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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: 16 September 2015

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Short-paper

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Telecom Paris-Tech Research Chair on Big Data and Market Insights.

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RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

Acceptance Rates

RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

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

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Citations

Cited By

Rahmani HAliannejadi MBaratchi MCrestani F(2022)A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest RecommendationACM Transactions on Information Systems10.1145/350847840:4(1-35)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3508478
Chakraborty AGanguly DCaputo AJones G(2022)Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendationInformation Retrieval10.1007/s10791-021-09400-925:1(44-90)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10791-021-09400-9
Liu YSong ZXu XRafique WZhang XShen JKhosravi MQi L(2022)Bidirectional GRU networks‐based next POI category prediction for healthcareInternational Journal of Intelligent Systems10.1002/int.2271037:7(4020-4040)Online publication date: 26-May-2022
https://dl.acm.org/doi/10.1002/int.22710
Li R(2021)Joint Modeling of User Behaviors Based on Variable-Order Additive Markov Chain for POI RecommendationWireless Communications & Mobile Computing10.1155/2021/43593692021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4359369
Guo YLiu FCai ZZeng HChen LZhou TXiao N(2021)PREFERProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34480995:1(1-25)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448099
Hu RLiu YLi YZhou JMa SXiong H(2020)Exploiting User Preference and Mobile Peer Influence for Human Mobility AnnotationACM Transactions on Knowledge Discovery from Data10.1145/340660014:6(1-18)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3406600
Wang PLiu KJiang LLi XFu YGupta RLiu YShah MRajan STang JPrakash B(2020)Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event StreamsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403128(853-861)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403128
Ge XChrysanthis PPelechrinis KZeinalipour-Yazti DSharaf M(2020)Serendipity-based Points-of-Interest NavigationACM Transactions on Internet Technology10.1145/339119720:4(1-32)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1145/3391197
Naumzik CZoechbauer PFeuerriegel S(2020)Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference ApproachProceedings of The Web Conference 202010.1145/3366423.3380298(2342-2353)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380298
Huang ZLin XLiu HZhang BChen YTang Y(2020)Deep Representation Learning for Location-Based RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29745347:3(648-658)Online publication date: Jun-2020
https://doi.org/10.1109/TCSS.2020.2974534
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