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Geographical recommender system based on interaction between map operation and category selection

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HetRec '10: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems

Pages 71 - 74

https://doi.org/10.1145/1869446.1869458

Published: 26 September 2010 Publication History

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Abstract

We propose a geographical information recommender system based on interaction between user's map operation and category selection. The system has three interfaces, the layered category interface, the geographical object interface and the digital map interface. Our system interactively updates each interface based on the category interest model and the region interest model. This paper describes each interface and each model, and how to update them by our system.

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Cited By

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Ebrahimian MKashef R(2020)Detecting Shilling Attacks Using Hybrid Deep Learning ModelsSymmetry10.3390/sym1211180512:11(1805)Online publication date: 31-Oct-2020
https://doi.org/10.3390/sym12111805
Ebrahimian MKashef R(2020)Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM45057.2020.9309965(460-464)Online publication date: 14-Dec-2020
https://doi.org/10.1109/IEEM45057.2020.9309965
Touloupos MKapassa EKyriazis DChristodoulou K(2020)Test Recommendation for Service Validation in 5G NetworksInformation Systems10.1007/978-3-030-44322-1_11(139-150)Online publication date: 18-Apr-2020
https://doi.org/10.1007/978-3-030-44322-1_11
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Index Terms

Geographical recommender system based on interaction between map operation and category selection
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information

Published In

HetRec '10: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems

September 2010

84 pages

ISBN:9781450304078

DOI:10.1145/1869446

Conference Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Iván Cantador
Universidad Autónoma de Madrid, Spain
,
Yehuda Koren
Yahoo! Labs
,
Tsvi Kuflik
University of Haifa, Israel
,
Markus Weimer
Yahoo! Labs

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26, 2010

Barcelona, Spain

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Cited By

View all

Ebrahimian MKashef R(2020)Detecting Shilling Attacks Using Hybrid Deep Learning ModelsSymmetry10.3390/sym1211180512:11(1805)Online publication date: 31-Oct-2020
https://doi.org/10.3390/sym12111805
Ebrahimian MKashef R(2020)Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM45057.2020.9309965(460-464)Online publication date: 14-Dec-2020
https://doi.org/10.1109/IEEM45057.2020.9309965
Touloupos MKapassa EKyriazis DChristodoulou K(2020)Test Recommendation for Service Validation in 5G NetworksInformation Systems10.1007/978-3-030-44322-1_11(139-150)Online publication date: 18-Apr-2020
https://doi.org/10.1007/978-3-030-44322-1_11
Zhang XChen DLiu J(2018)A Space-Time Periodic Task Model for Recommendation of Remote Sensing ImagesISPRS International Journal of Geo-Information10.3390/ijgi70200407:2(40)Online publication date: 29-Jan-2018
https://doi.org/10.3390/ijgi7020040
Suganeshwari GSyed Ibrahim S(2016)A Survey on Collaborative Filtering Based Recommendation SystemProceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’)10.1007/978-3-319-30348-2_42(503-518)Online publication date: 23-Feb-2016
https://doi.org/10.1007/978-3-319-30348-2_42
Bobadilla JOrtega FHernando AGutiéRrez A(2013)Recommender systems surveyKnowledge-Based Systems10.1016/j.knosys.2013.03.01246(109-132)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.knosys.2013.03.012
Oku KKotera RKitayama DSumiya K(2012)Geographical Recommender System Using User Interest Model Based on Map Operation and Category SelectionInternational Journal of Handheld Computing Research10.4018/jhcr.20120701013:3(1-16)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.4018/jhcr.2012070101
Kotera RKitayama DOku KSumiya KLee SHanzo LChung MLee SCho K(2011)Geographical recommendation method using user's interest model based on map operation and category selectionProceedings of the 5th International Conference on Ubiquitous Information Management and Communication10.1145/1968613.1968759(1-8)Online publication date: 21-Feb-2011
https://dl.acm.org/doi/10.1145/1968613.1968759

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Geographical Recommender System Using User Interest Model Based on Map Operation and Category Selection

Geographical recommendation method using user's interest model based on map operation and category selection

Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation