Development of an Arabic Pet Adoption System with Hybrid Recommendations

Abstract: Pet adoption platforms often face challenges in effectively matching adopters with suitable pets due to limited personalization and the lack of localized language support. This study presents EWAA, an Arabic-enabled mobile platform for pet adoption in Saudi Arabia that integrates a hybrid recommender system combining content-based and collaborative filtering techniques. The content-based component constructs a user profile from one-hot encoded pet attributes (category, breed, color, and age) of previously liked pets, while the collaborative filtering component identifies similar users through cosine similarity and recommends pets based on their preferences. The two components operate independently in a mixed hybrid configuration, which mitigates the cold-start limitation of collaborative filtering and the over-specialization limitation of content-based filtering. The platform also emphasizes usability, accessibility, and privacy for both adopters and pet owners through a fully Arabic interface and controlled information visibility. The system was evaluated with 20 questionnaire participants and 6 interview participants using User Acceptance Testing (UAT) and Non-Functional Requirements (NFR) testing. Results indicate high levels of user satisfaction, with task completion rates of 100% on 10 of 11 test scenarios, page load times between 1 and 12 seconds, and learning times between 1 and 8 minutes, suggesting that the proposed approach provides a viable foundation for supporting the pet adoption process in Arabic-speaking contexts.

Hailah Alballaa. “Development of an Arabic Pet Adoption System with Hybrid Recommendations”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170587

@article{Alballaa2026,
title = {Development of an Arabic Pet Adoption System with Hybrid Recommendations},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170587},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170587},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Hailah Alballaa}
}