Current movie-recommendation pipelines rely almost exclusively on linguistic features (plot synopses and user reviews) or collaborative signals. Cognitive narrative theory, however, suggests that narratives are defined by the effects they produce, rather than by surface features alone – phenomena like plot twists, irony, suspense, and reversal that modulate surprise and attention. Such effects define the distinguishing characteristics of narrative media as narrative, and necessarily contribute to viewer preferences. Because these effects are ignored by conventional approaches to film classification, though, recommender systems are effectively blind to this crucial basis of narrative comparison. We convert this insight into a computational asset by building an ontology of 321 such narrative elements, manually annotating 8,945 films. Integrated as symbolic priors into a simple ranked-filtering engine and benchmarked on the MovieLens protocol, our model yields a 7.5:1 ratio of liked to disliked recommendations while surfacing 60 percent previously unwatched titles – strong evidence that cognitively grounded features boost both relevance and novelty. Additionally, the distinctiveness of our classification system offers an opportunity to diversify discovery and recommendation. By relying on features that are not captured by content-based categories, such as genre, topic, or setting, it may help mitigate popularity, exposure, and expectation biases. The work offers empirical evidence for core hypotheses from narrative theory and positions narrative theory as a domain where machine learning can benefit from cognition-inspired structure, offering a tractable path toward recommender systems that optimize for user surprise and discovery rather than popularity bias.