Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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The Impact of Neighborhood Size Used in User-User Similarity Calculation on POI Recommendation Accuracy

Authors
Djelloul Bettache1, *, Nassim Dennouni2
1LME Laboratory, Hassiba Benbouali University, Chlef, Algeria
2Higher School of Management, Tlemcen, Algeria
*Corresponding author. Email: d.bettache@univ-chlef.dz
Corresponding Author
Djelloul Bettache
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_30How to use a DOI?
Keywords
Neighborhood size; similarity measure; Collaborative Filtering; Recommender System
Abstract

Point-of-interest (POI) recommendation systems help users discover locations that align with their interests and past behaviors. These systems often use Collaborative Filtering (CF), which relies on measuring similarities between users or items to make recommendations. The most common methods for assessing similarity are cosine similarity and Pearson correlation, which are crucial in making recommendations. However, the accuracy of these recommendations can vary based on the size of the “neighborhood” used in the CF models. This study examines how the neighborhood’s size influences recommendations’ accuracy in user-based CF models. We evaluate their impact on POI recommendations by comparing different similarity measures, including cosine similarity, Euclidean distance, and the Jaccard index. Our findings indicate that the ideal neighborhood size is closely tied to the specific similarity measure used. This highlights the importance of choosing the right neighborhood size in CF systems to ensure the best recommendation performance.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_30How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Djelloul Bettache
AU  - Nassim Dennouni
PY  - 2025
DA  - 2025/08/05
TI  - The Impact of Neighborhood Size Used in User-User Similarity Calculation on POI Recommendation Accuracy
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 267
EP  - 274
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_30
DO  - 10.2991/978-94-6463-805-9_30
ID  - Bettache2025
ER  -