Analysis of Deep Demographics for Advertisement Recommendation
บทความในวารสาร
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Kantinan Meesuk, Kittiphot Amnakkittikul, Nachanon Nuanphet, Warin Wattanapornprom, Thittaporn Ganokratanaa, Wachirapong Jirakitpuwapat
ผู้เผยแพร่: ResearchGate
ปีที่เผยแพร่ (ค.ศ.): 2025
Volume number: 11
หน้าแรก: 450
หน้าสุดท้าย: 462
จำนวนหน้า: 13
นอก: 2408-154X
eISSN: electronic journal
URL: https://bangmodjmcs.com/index.php/bangmodmcs/article/view/149
ภาษา: English-United States (EN-US)
บทคัดย่อ
Advertisements are a popular marketing strategy that shapes consumer perception and brand image. Consumers engage in outdoor advertising messages and traditional media advertisements. Understanding consumer behavior and interest in advertisements is crucial for developing effective marketing strategies. One study used computer vision techniques to analyze customer demographics, clothing preferences, and facial attention cues to extract comprehensive features from individuals and assess their attention toward advertisement displays. The methodology uses object detection models, such as YOLO, to track individuals in a scene, followed by a fashion detection model to identify clothing styles. The MiVOLO model predicts age and gender and creates a dataset for demographic analysis. ReginaFace is used for face detection and head pose estimation to gauge viewer engagement. This system helps retailers and advertisers tailor marketing strategies based on real-time customer data, providing insights into consumer preferences and interests. This enhanced customer engagement and sales.
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