Authors - Ashavaree Das, Dimo Valev, Sambhram Pattanayak, Prashant Kamal Abstract - The rise of short-form video (SFV) platforms like TikTok, Instagram Reels, and YouTube Shorts has caused a fundamental shift in digital marketing, moving from static images to engaging, multimodal strategies. These platforms utilize advanced "interest-graph" algorithms and unique user interfaces that significantly alter consumer attention spans and engagement patterns. Traditional marketing metrics often fall short in these environments, requiring new approaches that emphasize immediacy and authenticity. This paper explores the key intersection of algorithmic recommendation biases, content memorability, and technical video quality. To address these challenges, we propose an integrated framework that combines advanced blind video quality assessment (BVQA) with generative enhancements to optimize content for short-form formats. By incorporating technical insights from affective computing and recommender systems alongside strategic marketing goals, this study explores how "lo-fi" aesthetics and influencer-led credibility influence consumer attitudes. Our findings offer a roadmap for managing user-generated content (UGC) and algorithmic biases to enhance brand resonance and purchase intent in today's digital economy.
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