short-paper

Point-of-interest category recommendation based on group mobility modeling

Authors:

Xingxin Liu,

Xingyu Huang,

Yue Wang,

Lin ZhangAuthors Info & Claims

IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 39 - 40

https://doi.org/10.1145/3308557.3308670

Published: 16 March 2019 Publication History

Get Access

Abstract

With the development of location-based social networks (LBSNs), user mobility modeling has a wide range of applications, like performance-based advertising, city-wide traffic planning and local-based recommendation. Point-of-interest (POI) plays an important role in these applications for its rich semantic behavior information. However, most studies only use user's proactive POI check-ins which are extremely sparse and just a part of user's semantic offline behavior.

In this paper, we use POI category check-ins which are obtained by matching user's GPS data, and propose a framework based on Hidden Markov Model (HMM) to model group mobility and recommend the POI category of user at the next step. The category level of POI can reflect the semantic meaning of user mobility, and also reduce the recommendation space. Experiment results show that the recommendation accuracy is 11.9% higher with the group semantic offline behavior.

References

[1]

Ye, Jihang, Zhe Zhu, and Hong Cheng. 2013. What's your next move: User activity prediction in location-based social networks. In Proceedings of the 2013 SIAM International Conference on Data Mining (Society for Industrial and Applied Mathematics). 171--179.

Google Scholar

[2]

Lv, Qiujian, Yuanyuan Qiao, Nirwan Ansari, Jun Liu, and Jie Yang. 2017. Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest. IEEE Transactions on Vehicular Technology 66, 6 (2017), 5204--5216.

Crossref

Google Scholar

[3]

Chen, Jialiang, Xin Li, William K. Cheung, and Kan Li. 2016. Effective successive POI recommendation inferred with individual behavior and group preference. Neurocomputing, 210 (2016). 174--184.

Digital Library

Google Scholar

[4]

Mathew, Wesley, Ruben Raposo, and Bruno Martins. 2012. Predicting future locations with hidden Markov models. In Proceedings of the 2012 ACM conference on ubiquitous computing. ACM, 911--918.

Digital Library

Google Scholar

Cited By

View all

Nakamura RSano HInagaki AOsawa RTakagi TMunemasa I(2021)Dynamic Topic-Enhanced Memory Networks: Time-series Behavior Prediction based on Changing Intrinsic Consciousnesses2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00035(185-191)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00035
Osawa RSuekane KNakamura RInagaki ATakagi TMunemasa I(2021)Predicting Human Behavior with Transformer Considering the Mutual Relationship between Categories and Regions2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00029(144-150)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00029
Yu RYe DLi J(2020)RePiDeM: A Refined POI Demand Modeling based on Multi-Source Data^*IEEE INFOCOM 2020 - IEEE Conference on Computer Communications10.1109/INFOCOM41043.2020.9155294(964-973)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1109/INFOCOM41043.2020.9155294

Index Terms

Point-of-interest category recommendation based on group mobility modeling
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

Recommendations

Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation

Modeling the topic model.Modeling the geographical correlations.Modeling the social correlations.Modeling the categorical correlations.We integrate the textual, geographical, social, categorical and popularity information into probabilistic matrix ...
Discovering Point-of-Interest Signatures Based on Group Features from Geo-social Networking Data
TAAI '13: Proceedings of the 2013 Conference on Technologies and Applications of Artificial Intelligence

In recent years, location-based social networking services (LBSNSs) have become popular, generating a huge volume of geo-social networking data, such as check-in records and geo-tagged photos. The geo-social networking data provide a new source for ...
Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...

Comments

Information & Contributors

Information

Published In

IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

173 pages

ISBN:9781450366731

DOI:10.1145/3308557

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

IUI '19

Sponsor:

IUI '19: 24th International Conference on Intelligent User Interfaces

March 16 - 20, 2019

California, Marina del Ray

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
197
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Nakamura RSano HInagaki AOsawa RTakagi TMunemasa I(2021)Dynamic Topic-Enhanced Memory Networks: Time-series Behavior Prediction based on Changing Intrinsic Consciousnesses2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00035(185-191)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00035
Osawa RSuekane KNakamura RInagaki ATakagi TMunemasa I(2021)Predicting Human Behavior with Transformer Considering the Mutual Relationship between Categories and Regions2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00029(144-150)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00029
Yu RYe DLi J(2020)RePiDeM: A Refined POI Demand Modeling based on Multi-Source Data^*IEEE INFOCOM 2020 - IEEE Conference on Computer Communications10.1109/INFOCOM41043.2020.9155294(964-973)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1109/INFOCOM41043.2020.9155294

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation

Discovering Point-of-Interest Signatures Based on Group Features from Geo-social Networking Data

Time-aware point-of-interest recommendation

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations