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Cited by 48Recommender Systems and TechniquesHuman Mobility and Location-Based AnalysisInformation Retrieval and Search Behavior

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The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems

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Rahmani, Hossein A., Deldjoo, Yashar, di Noia, Tommaso·2022·Expert Systems with Applications
DOI 10.1016/j.eswa.2022.117700
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Abstract

Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. Previous studies have shown that incorporating contextual information such as geographical, temporal, social, and categorical substantially improves the quality of POI recommendations. However, fewer works have studied in-depth the multi-aspect benefits of context fusion on POI recommendation, in particular on beyond-accuracy, fairness, and interpretability of recommendations. In this work, we propose a linear regression-based fusion of POI contexts that effectively finds the best combination of contexts for each (i) user, or (ii) group of users from their historical interactions. The results of large-scale experiments on two popular datasets Gowalla and Yelp reveal several interesting findings. First, the proposed approach does not present significant loss in accuracy and unfairness of popularity bias as with classical collaborative baselines, and yet improves the beyond-accuracy of recommendation compared with existing context-aware (CA) approaches using heuristic context fusions; for instance, the proposed approach improves the accuracy and beyond-accuracy compare to best baseline model by 25% and 30%, respectively. Second, our pr…

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Research works printed but not linked: 27

  1. Adamczak, 2020, Session-based hotel recommendations dataset: As part of the ACM recommender system challenge 2019, ACM Transactions on Intelligent Systems and Technology (TIST), 12, 1
  2. Altan, 2019, The effect of kernel values in support vector machine to forecasting performance of financial time series, The Journal of Cognitive Systems, 4, 17
  3. Balog, K., Radlinski, F., & Arakelyan, S. (2019). Transparent, scrutable and explainable user models for personalized recommendation. In Proceedings of the 42nd international acm sigir conference on research and development in information retrieval (pp. 265–274). · DOI
  4. Baral, 2018, Exploiting the roles of aspects in personalized POI recommender systems, Data Mining and Knowledge Discovery, 32, 320, 10.1007/s10618-017-0537-7 · DOI
  5. Dacrema, M. F., Cremonesi, P., & Jannach, D. (2019). Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM conference on recommender systems (pp. 101–109). · DOI
  6. Deldjoo, 2021, A flexible framework for evaluating user and item fairness in recommender systems, User Modeling and User-Adapted Interaction, 1
  7. Deldjoo, 2021, Explaining recommender systems fairness and accuracy through the lens of data characteristics, Information Processing & Management, 58, 10.1016/j.ipm.2021.102662 · DOI
  8. Deldjoo, Y., Di Noia, T., Di Sciascio, E., & Merra, F. A. (2020). How dataset characteristics affect the robustness of collaborative recommendation models. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. · DOI
  9. Deldjoo, 2020, Recommender systems leveraging multimedia content, ACM Computing Surveys, 53, 1, 10.1145/3407190 · DOI
  10. Ekstrand, M. D. (2020). LensKit for python: Next-generation software for recommender systems experiments. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 2999–3006). · DOI
  11. Ge, M., Delgado-Battenfeld, C., & Jannach, D. (2010). Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on recommender systems (pp. 257–260). · DOI
  12. Kaminskas, 2016, Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems, ACM Transactions on Interactive Intelligent Systems (TiiS), 7, 1
  13. Karasu, 2020, A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series, Energy, 212, 10.1016/j.energy.2020.118750 · DOI
  14. Liu, 2017, An experimental evaluation of point-of-interest recommendation in location-based social networks, Proceedings of the VLDB Endowment, 10, 1010, 10.14778/3115404.3115407 · DOI
  15. Manotumruksa, 2020, A contextual recurrent collaborative filtering framework for modelling sequences of venue checkins, Information Processing & Management, 57, 10.1016/j.ipm.2019.102092 · DOI
  16. Massimo, 2021, Popularity, novelty and relevance in point of interest recommendation: an experimental analysis, Information Technology & Tourism, 23, 473, 10.1007/s40558-021-00214-5 · DOI
  17. Quadrana, 2018, Sequence-aware recommender systems, ACM Computing Surveys, 51, 1, 10.1145/3190616 · DOI
  18. Rahmani, H. A., Aliannejadi, M., Mirzaei Zadeh, R., Baratchi, M., Afsharchi, M., & Crestani, F. (2019). Category-aware location embedding for point-of-interest recommendation. In Proceedings of the 2019 ACM SIGIR international conference on theory of information retrieval (pp. 173–176). · DOI
  19. Salah, 2020, Cornac: A comparative framework for multimodal recommender systems, Journal of Machine Learning Research, 21, 1
  20. Sánchez, P. (2019). Exploiting contextual information for recommender systems oriented to tourism. In Proceedings of the 13th ACM conference on recommender systems (pp. 601–605). · DOI
  21. Sánchez, 2021, On the effects of aggregation strategies for different groups of users in venue recommendation, Information Processing & Management, 58, 10.1016/j.ipm.2021.102609 · DOI
  22. Sezer, 2021, Detection of solder paste defects with an optimization-based deep learning model using image processing techniques, Soldering & Surface Mount Technology, 10.1108/SSMT-04-2021-0013 · DOI
  23. Werneck, 2021, Effective and diverse POI recommendations through complementary diversification models, Expert Systems with Applications, 175, 10.1016/j.eswa.2021.114775 · DOI
  24. Weydemann, L., Sacharidis, D., & Werthner, H. (2019). Defining and measuring fairness in location recommendations. In Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based recommendations, geosocial networks and geoadvertising (pp. 1–8). · DOI
  25. Yang, L., Bagdasaryan, E., Gruenstein, J., Hsieh, C.-K., & Estrin, D. (2018). Openrec: A modular framework for extensible and adaptable recommendation algorithms. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 664–672). · DOI
  26. Ye, M., Yin, P., Lee, W.-C., & Lee, D.-L. (2011). Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval (pp. 325–334). · DOI
  27. Zhang, J.-D., & Chow, C.-Y. (2013). iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 334–343). · DOI

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Metadata

Authors
Rahmani, H. A., Deldjoo, Y., & di Noia, T.
Affiliations
  • University College London (GB)
  • Polytechnic University of Bari (IT)
Corresponding author
  • Rahmani, Hossein A.
  • Deldjoo, Yashar
ORCID
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2022-11
Volume
205
Pages
117700
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2022.117700
OpenAlex
W4281619382
Cited by
48
References
1
Citations by year
2026: 6 · 2025: 9 · 2024: 16 · 2023: 13 · 2022: 4
FWCI
10.40
Type
journal article
Language
en
Open access
Licence
Locations
Expert Systems with Applications (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
point-of-interest recommendation, context fusion, location-based social networks, recommendation fairness, popularity bias, interpretability, explainable recommendation, collaborative filtering, LORE, user activity level

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