[
    {
        "id": "osp-14277",
        "type": "article-journal",
        "title": "Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics",
        "author": [
            {
                "family": "Boughareb",
                "given": "Djalila"
            },
            {
                "family": "Bensalah",
                "given": "Hazem"
            },
            {
                "family": "Kouahla",
                "given": "Zineddine"
            }
        ],
        "URL": "https://omanscience.com/en/articles/collaborative-advertisement-recommendation-system-leveraging-user-preferences-geography-and-demographics",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2025
                ]
            ]
        },
        "container-title": "Journal of Business, Communication and Technology",
        "volume": "4",
        "issue": "1",
        "page": "18-31",
        "DOI": "10.56632/bct.2025.4102",
        "publisher": "Gulf College",
        "abstract": "The increasing demand for personalized advertising based on user preferences is driving a surge in popularity. Social networks utilize millions of user’ data to suggest ads based on specific criteria. However, many of these ads can be uninteresting. This paper presents a collaborative advertisement recommendation system that leverages users’ preferences along with geographic and demographic data to deliver engaging ads. The system employs the K-dtree algorithm to efficiently organize users into interest-based communities and model complex patterns within those communities to enhance ad relevance. The dataset, collected via Hazmit provides a rich source of information. The system’s performance was evaluated based on precision, recall, F-score, and accuracy metrics, as well as running time measurements. The results highlighted the superior effectiveness of the K-dtree-based approach in accurately targeting the right customers for advertisements. Overall, the K-dtree method improves ad targeting accuracy, especially for food and demographics, but struggles with news due to subjectivity and regional biases."
    }
]