Ocean Of Movies ((better)) (2025)
Ocean of Movies — Short Paper
Abstract
"Ocean of Movies" examines the dynamics of large-scale movie discovery platforms that aggregate films, user preferences, and metadata. This paper proposes a hybrid recommendation framework combining content-based filtering, collaborative filtering, and knowledge-graph enrichment to improve discovery, address cold-start problems, and surface diverse, long-tail films.
The Undercurrents: Binge-Watching vs. Deep Watching
One of the most dangerous riptides in the modern ocean of movies is the erosion of attention. We have been trained by television (6-hour seasons) to consume media passively. Movies demand a different physiology. ocean of movies
Description: Imagine having access to a vast, ever-expanding ocean of movies, where you can dive in and discover new favorites, explore different genres, and embark on thrilling adventures. With our "Ocean of Movies" feature, you'll experience an immersive and engaging way to enjoy your favorite films. Ocean of Movies — Short Paper Abstract "Ocean
This draft outlines a hypothetical feature film that captures the beauty and terror of the open water, drawing inspiration from modern ocean cinema like Life of Pi All Is Lost Koren et al
References (suggested)
- Koren et al., Matrix Factorization Techniques for Recommender Systems.
- He et al., Neural Collaborative Filtering.
- Wang et al., Knowledge-Aware Graph Neural Networks for Recommendations.
- Rendle, Factorization Machines; McNee et al., On the recommending of items.