This paper outlines a novel architecture blueprint that use large language models (LLMs) to enhance ad targeting effectiveness through personalized messaging.
In 2017, Netflix demonstrated the impact of personalized promotional messaging by adapting artwork based on user preferences and viewing history. The company’s content experts generated multiple images for every title and change them regularly to lure audiences based on their previous viewing history. The approach utilized a sophisticated “online reinforcement learning” strategy, optimizing the balance between exploiting known user preferences and exploring new data for improved recommendations. This dynamic methodology was essential in minimizing the cumulative “regret” (defined as the difference between the expected “payoff” (e.g. engagement) of the algorithm and the payoff of a single fixed strategy for selecting artworks) and enhancing viewer satisfaction over time.
Despite its efficiency, this approach epitomized a few key principles under the legacy Personalization 1.0 paradigm, namely the need to choose between a finite set of creatives (while being limited by multimodal content creation’s cost and complexity), the reliance on high-quality (and therefore expensive) human preference labels to optimize algorithmic tuning, and the use of shallow context information (e.g. transient signals like search history) as proxies for viewers’ interests. Today’s advancements in Generative AI necessitate a complete re evaluation of the algorithmic / architectural trade-off and its relevance.
This paper outlines...
Exclusive Content
This article is available with a Technical Paper Pass
.jpg)
Selective multi-pass encoding for cost-effective video streaming
Tech Papers 2026: This paper presents a content-adaptive strategy, CASE, that predicts whether additional encoding passes would provide meaningful gains using a lightweight mechanism that derives spatial and temporal features from each video segment.
.jpg)
Scalable SSIM estimation from PSNR for per-title and context adaptive encoding workflows
Tech Papers 2026: This paper proposes ApproxSSIMate, a low-complexity method for estimating SSIM from PSNR combined with reference-sequence statistics.
.jpg)
Feasibility and deployment strategies for cloud-based AOIP audio consoles
Tech Papers 2026: This paper investigates the feasibility of cloud-based audio-mixing systems by re-examining existing assumptions about network conditions, multicast transport and synchronisation.
.jpg)
A 100 Hz frame-interleaved approach to live multi-camera switching in led-based virtual production: Experience from a public-broadcaster proof of concept
Tech Papers 2026: This paper reports a Virtual Production (VP) proof of concept carried out by SWR, a German public-service broadcaster, under real broadcast conditions.
.jpg)
Transforming broadcast TV in Ireland: A path to an IP future
Tech Papers 2026: This paper gives an overview of how Ireland is working towards the transformation of free-to-air public service broadcasting to include greater access to internet-based content delivery.
