Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics
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...
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| Format: | Article |
| Language: | English |
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Gulf College
2025-06-01
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| Series: | Journal of Business, Communication and Technology |
| Subjects: | |
| Online Access: | https://bctjournal.com/article_459_0db44d8df19c70af15406aee1c4589d5.pdf |
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| _version_ | 1850111278126727168 |
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| author | Djalila Boughareb Hazem Bensalah Zineddine Kouahla |
| author_facet | Djalila Boughareb Hazem Bensalah Zineddine Kouahla |
| author_sort | Djalila Boughareb |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-799d69896e604c73bc9db1b53df575e4 |
| institution | OA Journals |
| issn | 2791-3775 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Gulf College |
| record_format | Article |
| series | Journal of Business, Communication and Technology |
| spelling | doaj-art-799d69896e604c73bc9db1b53df575e42025-08-20T02:37:39ZengGulf CollegeJournal of Business, Communication and Technology2791-37752025-06-0141183110.56632/bct.2025.4102459Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and DemographicsDjalila Boughareb0Hazem Bensalah1Zineddine Kouahla2University of 8 Mai 1945, AlgeriaUniversity of El Oued, AlgeriaUniversity of 8 Mai 1945, AlgeriaThe 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.https://bctjournal.com/article_459_0db44d8df19c70af15406aee1c4589d5.pdfrecommender systemsadvertisement recommendationk-dtreecollaborative filtering |
| spellingShingle | Djalila Boughareb Hazem Bensalah Zineddine Kouahla Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics Journal of Business, Communication and Technology recommender systems advertisement recommendation k-dtree collaborative filtering |
| title | Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics |
| title_full | Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics |
| title_fullStr | Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics |
| title_full_unstemmed | Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics |
| title_short | Collaborative Advertisement Recommendation System Leveraging User Preferences, Geography, and Demographics |
| title_sort | collaborative advertisement recommendation system leveraging user preferences geography and demographics |
| topic | recommender systems advertisement recommendation k-dtree collaborative filtering |
| url | https://bctjournal.com/article_459_0db44d8df19c70af15406aee1c4589d5.pdf |
| work_keys_str_mv | AT djalilaboughareb collaborativeadvertisementrecommendationsystemleveraginguserpreferencesgeographyanddemographics AT hazembensalah collaborativeadvertisementrecommendationsystemleveraginguserpreferencesgeographyanddemographics AT zineddinekouahla collaborativeadvertisementrecommendationsystemleveraginguserpreferencesgeographyanddemographics |