research-article

Modeling Location-Based User Rating Profiles for Personalized Recommendation

Authors:

Chengqi ZhangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 9, Issue 3

Article No.: 19, Pages 1 - 41

https://doi.org/10.1145/2663356

Published: 01 April 2015 Publication History

Abstract

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem.

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Cited By

Dietz LSánchez PBellogín A(2025)Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithmsInformation Technology & Tourism10.1007/s40558-024-00304-0Online publication date: 3-Jan-2025
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An JGao MTang J(2024)MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI RecommendationACM Transactions on Information Systems10.1145/366465142:6(1-29)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664651
Lai YSu YWei LWang TZha DWang X(2024)Adaptive Spatial-Temporal Hypergraph Fusion Learning for Next POI RecommendationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447357(7320-7324)Online publication date: 14-Apr-2024
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Index Terms

Modeling Location-Based User Rating Profiles for Personalized Recommendation
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 9, Issue 3

TKDD Special Issue (SIGKDD'13)

April 2015

313 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2737800

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Copyright © 2015 ACM.

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Publication History

Published: 01 April 2015

Accepted: 01 August 2014

Revised: 01 July 2014

Received: 01 March 2014

Published in TKDD Volume 9, Issue 3

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Funding Sources

National Natural Science Foundation of China
ARC Discovery Project)
973 program
Australian Research Council (ARC)
Chinese National “111” project, “Attracting International Talents in Data Engineering and Knowledge Engineering Research.”

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An JGao MTang J(2024)MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI RecommendationACM Transactions on Information Systems10.1145/366465142:6(1-29)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664651
Lai YSu YWei LWang TZha DWang X(2024)Adaptive Spatial-Temporal Hypergraph Fusion Learning for Next POI RecommendationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447357(7320-7324)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447357
Zhou JLu J(2024)STAC-HNN: a spatio-temporal auto-correlation based location recommendation using hypergraph neural networkEvolving Systems10.1007/s12530-024-09650-016:1Online publication date: 19-Dec-2024
https://doi.org/10.1007/s12530-024-09650-0
Li YZhao QLin CZhang ZZhu XSu J(2024)GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-trainingDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_24(343-352)Online publication date: 2-Jul-2024
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Chen LLyu DYu SChen G(2023)Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged PhotosACM Transactions on Knowledge Discovery from Data10.1145/358201517:7(1-18)Online publication date: 6-Apr-2023
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Lai YSu YWei LChen GWang TZha D(2023)Multi-view Spatial-Temporal Enhanced Hypergraph Network for Next POI RecommendationDatabase Systems for Advanced Applications10.1007/978-3-031-30672-3_16(237-252)Online publication date: 17-Apr-2023
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Gao ZLiu ZAn J(2022)Cyber-Physical-Social Model for Service Recommendation in the Internet of ThingsMathematical Problems in Engineering10.1155/2022/95239842022(1-13)Online publication date: 10-May-2022
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