Does Context Matter? The Role of Fine-Tuned Contextual Augmentation in Online Ad Delivery on Social Media

Abstract

This study presents a real-time, context-adaptive advertisement (ad in short) recommendation framework that dynamically updates user context and utilizes a multistage ranking and filtering pipeline to deliver highly relevant and personalized ads. Contextual ads contribute to better conversion rates and play a significant role in e-commerce. In contrast, non-contextual ads engender frustration among advertisers and users: commercialization efforts frequently prove ineffective due to poor user engagement, as evidenced by high ad-skipping rates. The current practices in digital advertising involve non-contextual and irrelevant ads, which result in poor conversion rates. To address this problem, this article explores semantically enriched and context-aware recommender systems, aiming to align ads with user interests. The proposed framework investigates various components, including a user context extractor (UCE), recommender system, ads database, ads ranker, and ads filter. This study also explores how high-quality and relevant content, along with clickable advertising, contributes to improving customer relationships and reducing ad avoidance. During contextual augmentation, ads that become relevant and engaging are projected to have increased click-through rates in a real-world application. Customer engagement and satisfaction would also increase due to a reduction in ad fatigue and the delivery of relevant content. Furthermore, it can curb ad avoidance because users will gladly respond to ads that suit their interests. Businesses make higher conversions because the more relevant recommendation means greater user interaction. The proposed framework combines a UCE, an ad database, a ranking mechanism, and a filtering module to deliver real-time, personalized recommendations. Evaluated using a k-nearest neighbor-based model, the system achieved improved precision (from 0.8275 to 0.9283), recall (from 0.4628 to 0.5201), and normalized discounted cumulative gain (from 0.9906 to 0.9915). These gains demonstrate that integrating fine-grained, dynamic user context substantially enhances recommendation quality and user engagement, offering a scalable foundation for intelligent, adaptive advertising systems. This research contributes toward the future development of an AI-enabled advertising strategy, with an emphasis on dynamic ad targeting that goes hand in hand with personalization and thus improved conversion rate.