We propose Image Content Appeal Assessment (ICAA), a novel metric focused on quantifying the level of positive interest an image's content generates for viewers, such as the appeal of food in a photograph. This new metric is fundamentally different from traditional Image-Aesthetics Assessment (IAA), which judges an image's artistic quality. While previous studies have often confused the concepts of aesthetics'' andappeal,'' our work addresses this oversight by being the first to study ICAA explicitly. To do this, we propose a novel system that automates dataset creation, avoids extensive manual labeling work, and implements algorithms to estimate and boost content appeal. We use our pipeline to generate two large-scale datasets (70K+ images each) in diverse domains (food and room interior design) to train our models, which revealed little correlation between content appeal and aesthetics. Our user study, with more than 76% of participants preferring the appeal-enhanced images, confirms that our appeal ratings accurately reflect user preferences, establishing ICAA as a unique evaluative criterion.