A reproducible POI recommendation framework: Works mapping and benchmark evaluation. Issue 108 (September 2022)
- Record Type:
- Journal Article
- Title:
- A reproducible POI recommendation framework: Works mapping and benchmark evaluation. Issue 108 (September 2022)
- Main Title:
- A reproducible POI recommendation framework: Works mapping and benchmark evaluation
- Authors:
- Werneck, Heitor
Silva, Nícollas
Pereira, Adriano
Carvalho, Matheus
Bellogín, Alejandro
Martinez-Gil, Jorge
Mourão, Fernando
Rocha, Leonardo - Abstract:
- Abstract: This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering different aspects besides accuracy. Due to the lack of reproducible and extensible benchmarks, our work introduces a reproducibility framework for POI methods based on a collection of Python software libraries and a Docker image. Our proposal is composed of: (1) a package to perform a protocol that reproduces our systematic mapping process Werneck et al. (2021), containing all collected data, insightful views on current advances and opened challenges; and (2) an extensible benchmark to perform a protocol to reproduce experimental evaluations on POI recommendation, considering different datasets, metrics, and the strongest baselines in the literature. This work also demonstrates all processes required to instantiate its framework. Moreover, our work can be considered at least weakly reproducible, since we were able to reproduce the results of the previous paper, leading us to the same conclusions. Highlights: A companion reproducibility paper about POI recommendation. An extensible benchmark about POI recommendation. A collection of Python software libraries and a Docker image. A package to perform a protocol that reproducesAbstract: This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering different aspects besides accuracy. Due to the lack of reproducible and extensible benchmarks, our work introduces a reproducibility framework for POI methods based on a collection of Python software libraries and a Docker image. Our proposal is composed of: (1) a package to perform a protocol that reproduces our systematic mapping process Werneck et al. (2021), containing all collected data, insightful views on current advances and opened challenges; and (2) an extensible benchmark to perform a protocol to reproduce experimental evaluations on POI recommendation, considering different datasets, metrics, and the strongest baselines in the literature. This work also demonstrates all processes required to instantiate its framework. Moreover, our work can be considered at least weakly reproducible, since we were able to reproduce the results of the previous paper, leading us to the same conclusions. Highlights: A companion reproducibility paper about POI recommendation. An extensible benchmark about POI recommendation. A collection of Python software libraries and a Docker image. A package to perform a protocol that reproduces our systematic mapping process. A detailed reproducibility analysis of the primary work. … (more)
- Is Part Of:
- Information systems. Issue 108(2022)
- Journal:
- Information systems
- Issue:
- Issue 108(2022)
- Issue Display:
- Volume 108, Issue 108 (2022)
- Year:
- 2022
- Volume:
- 108
- Issue:
- 108
- Issue Sort Value:
- 2022-0108-0108-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-09
- Subjects:
- POI recommendation -- Benchmark -- Works mapping
Database management -- Periodicals
Electronic data processing -- Periodicals
Bases de données -- Gestion -- Périodiques
Informatique -- Périodiques
Database management
Electronic data processing
Periodicals
005.7 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064379 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.is.2022.102019 ↗
- Languages:
- English
- ISSNs:
- 0306-4379
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4496.367300
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British Library HMNTS - ELD Digital store - Ingest File:
- 21565.xml