[Project] Advanced Point-of-Interest (PoI) Recommendation Ecosystem
January 2021 - February 2022
Affiliation: Independent Research Initiative
Target Audience: Recommender Systems (RecSys) Researchers & Location-Based Service Providers
Project Ecosystem: GitHub (CAPRI) | GitHub (FairPOI) | GitHub (ContextsFair) | Paper (CAPRI) | Paper (FairPOI) | Paper (ContextsFair)
- Context-Aware Recommendation Engine: spatio-temporal modeling techniques to suggest location-specific points of interest based on complex user history and contextual configurations.
- Algorithmic Fairness & Bias Mitigation: investigates data biases in geographic recommendations, formulating dedicated fairness metrics to ensure balanced exposure for vendor items.
- Synthetic Generation: developed a custom architectural pipeline for generating realistic synthetic PoI recommendation traces.
- Peer-Reviewed Open Source: released distinct codebase suites to the global RecSys research community, backed by multiple publications in prominent peer-reviewed venues.
- 💡 Stack: Python (Data Science Stack)
A. Tourani, H.A. Rahmani, M. Naghiaei, and Y. Deldjoo,
"CAPRI: Context-aware Point-of-Interest Recommendation Framework,"
Software Impacts, vol. 19, p. 100606, 2024.
DOI: 10.1016/j.simpa.2023.100606
DOI: 10.1016/j.simpa.2023.100606
H.A. Rahmani, M. Naghiaei, A. Tourani, and Y. Deldjoo,
"Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation,"
Proceedings of the 16th ACM Conference on Recommender Systems (RecSys'22),,
pp. 598-603, Seattle, WA, USA, 2022.
DOI: 10.1145/3523227.3551481
DOI: 10.1145/3523227.3551481
H.A. Rahmani, Y. Deldjoo, A. Tourani, and M. Naghiaei,
"The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation,"
International Workshop on Algorithmic Bias in Search and Recommendation (BIAS'22),
pp. 56-68, 2022.
DOI: 10.1007/978-3-031-09316-6_6
DOI: 10.1007/978-3-031-09316-6_6