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Geographic-categorical diversification in POI recommendations

Authors:

Rodrigo Carvalho,

Nícollas Silva,

Adriano C. M. Pereira,

Leonardo RochaAuthors Info & Claims

WebMedia '19: Proceedings of the 25th Brazillian Symposium on Multimedia and the Web

Pages 349 - 356

https://doi.org/10.1145/3323503.3349554

Published: 29 October 2019 Publication History

Abstract

Nowadays, Recommender Systems (RSs) have been used to help users to discover relevant Points Of Interest (POI) in Location Based Social Network (LBSN), such as Yelp and FourSquare. Due to the main challenges of data sparsity and the geographic influence in this scenario, most of works about POI recommendations has only focused on improving the system's accuracy. However, there is a consensus that just it is not enough to assess the practical effectiveness. In real scenarios, categorical and geographic diversities have been identified as key dimensions of recommendation utility. The few existing works are concentrated on just one of these concepts, singly. In this work, we propose a novel post-processing strategy to merge these concepts in order to improve the user interest in POIs. Our experimental results in Yelp datasets show that our strategy can improve users' satisfaction, considering different RS and multiple diversification metrics. Our method is able to improve the diversity up to 120% without significant accuracy losses.

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Cited By

Zheng XYou HHuang HSun LYu QLuo Y(2024)Two-stage greedy algorithm based on crowd sensing for tour route recommendationApplied Soft Computing10.1016/j.asoc.2024.111260153:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111260
Bittencourt GFonseca GAndrade YSilva NRocha L(2023)A Survey on Review - Aware Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617050(198-207)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617050
Silva TSilva NMito CPereira ARocha L(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
https://dl.acm.org/doi/10.1145/3539637.3557060
Show More Cited By

Index Terms

Geographic-categorical diversification in POI recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Geographical Diversification in POI Recommendation: Toward Improved Coverage on Interested Areas
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

In recommending POIs(Point-Of-Interests), factors such as the diversity of the recommended POIs are as important as accuracy for providing a satisfactory recommendation. Although existing diversification methods can help POI recommender systems suggest ...
Geographic Diversification of Recommended POIs in Frequently Visited Areas

In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users’ frequently visited areas, because users are ...
Topic-based User Profile Model for POI Recommendations
ISMSI '18: Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

Location-based services (LBSs) provide users with points of interest (POIs) based on a limited distance from the user current location. Recently, the increased number of POIs in LBSs often results in overwhelming place suggestions generated to users ...

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Published In

cover image ACM Other conferences

WebMedia '19: Proceedings of the 25th Brazillian Symposium on Multimedia and the Web

October 2019

537 pages

ISBN:9781450367639

DOI:10.1145/3323503

General Chairs:
Joel dos Santos
CEFET/RJ
,
Débora Christina Muchaluat Saade
UFF
,
Maria da Graça C. Pimentel
University of Sao Paulo, Brazil
,
Alessandra Alaniz Macedo
University of Sao Paulo, Brazil

Copyright © 2019 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2019

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WebMedia '19

WebMedia '19: Brazilian Symposium on Multimedia and the Web

October 29 - November 1, 2019

Rio de Janeiro, Brazil

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Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Zheng XYou HHuang HSun LYu QLuo Y(2024)Two-stage greedy algorithm based on crowd sensing for tour route recommendationApplied Soft Computing10.1016/j.asoc.2024.111260153:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111260
Bittencourt GFonseca GAndrade YSilva NRocha L(2023)A Survey on Review - Aware Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617050(198-207)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617050
Silva TSilva NMito CPereira ARocha L(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
https://dl.acm.org/doi/10.1145/3539637.3557060
Werneck HSilva NViana MMourão FPereira ARocha Lde Salles Soares Neto C(2020)A Survey on Point-of-Interest Recommendation in Location-based Social NetworksProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430970(185-192)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428658.3430970

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