skip to main content
10.1145/3428658.3431754acmconferencesArticle/Chapter ViewAbstractPublication PageswebmediaConference Proceedingsconference-collections
short-paper

Combining complementary diversification models for personalized POI recommendations

Published: 30 November 2020 Publication History

Abstract

Location-Based Social Networks (LBSNs) have become important tools for people interested in exploring new places. And, similar to traditional recommendation domains, handling the trade-off between accuracy and diversity is a major challenge to provide useful recommendations. However, this domain adds an equally relevant dimension to this challenge: the geographical distance between users and each point-of-interest (POI). Besides understanding how the characteristics of services offered by each POI fit the user needs, realizing how far users are willing to move to fulfill these needs is of paramount relevance. Moreover, the users present distinct levels of interest in diversification. In this paper, we propose a strategy to provide POI recommendations linearly combining categorical and geographical diversifications in a personalized way. Indeed, our strategy is able to prioritize quality dimensions that better suit the personalized needs of each user, with gains up to 10% when compared with unpersonalized versions.

References

[1]
Rodrigo Carvalho, Nícollas Silva, Luiz Chaves, Adriano C. M. Pereira, and Leonardo C. da Rocha. 2019. Geographic-categorical diversification in POI recommendations. In Proceedings of the 25th WebMedia, Rio de Janeiro, Brazil, October 29 - November 01, 2019. ACM, 349--356.
[2]
Van Dang and W Bruce Croft. 2012. Diversity by proportionality: an election-based approach to search result diversification. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, 65--74.
[3]
Jungkyu Han and Hayato Yamana. 2017. Geographical diversification in POI recommendation: toward improved coverage on interested areas. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 224--228.
[4]
Matevž Kunaver and Tomaž Požrl. 2017. Diversity in recommender systems--A survey. Knowledge-Based Systems 123 (2017), 154--162.
[5]
Kyunghan Lee, Seongik Hong, Seong Joon Kim, Injong Rhee, and Song Chong. 2009. Slaw: A new mobility model for human walks. In IEEE INFOCOM 2009. IEEE, 855--863.
[6]
Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1043--1051.
[7]
Feng Lu and Nava Tintarev. 2018. A Diversity Adjusting Strategy with Personality for Music Recommendation. In IntRS@ RecSys. 7--14.
[8]
Xiangfu Meng, Yanhuan Tang, and Xiaoyan Zhang. 2017. DP-POIRS: A Diversified and Personalized Point-of-Interest Recommendation System. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 332--333.
[9]
Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al. 2019. Personalized re-ranking for recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 3--11.
[10]
Shameem A Puthiya Parambath, Nicolas Usunier, and Yves Grandvalet. 2016. A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 15--22.
[11]
Tieyun Qian, Bei Liu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2019. Spatiotemporal representation learning for translation-based POI recommendation. ACM Transactions on Information Systems (TOIS) 37, 2 (2019), 1--24.
[12]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, 1--35.
[13]
Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, and Pablo Castells. 2014. Coverage, redundancy and size-awareness in genre diversity for recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 209--216.
[14]
Saúl Vargas and Pablo Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 109--116.
[15]
Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, 458--461.

Cited By

View all
  • (2022)Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenarioProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3557060(211-221)Online publication date: 7-Nov-2022

Index Terms

  1. Combining complementary diversification models for personalized POI recommendations

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WebMedia '20: Proceedings of the Brazilian Symposium on Multimedia and the Web
    November 2020
    364 pages
    ISBN:9781450381963
    DOI:10.1145/3428658
    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]

    Sponsors

    In-Cooperation

    • SBC: Brazilian Computer Society
    • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
    • CGIBR: Comite Gestor da Internet no Brazil
    • CAPES: Brazilian Higher Education Funding Council

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 November 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. POI recommendation
    2. Personalized
    3. Recommender Systems

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Conference

    WebMedia '20
    Sponsor:
    WebMedia '20: Brazillian Symposium on Multimedia and the Web
    November 30 - December 4, 2020
    São Luís, Brazil

    Acceptance Rates

    WebMedia '20 Paper Acceptance Rate 34 of 87 submissions, 39%;
    Overall Acceptance Rate 270 of 873 submissions, 31%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)6
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenarioProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3557060(211-221)Online publication date: 7-Nov-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media