AI in Digital Entertainment: Exploring User-Centric Movie Prediction Systems
Bibliographic record
Abstract
ABSTRACT This paper explores the development and implementation of a movie recommendation system powered by Artificial Intelligence (AI), focusing on the use of collaborative filtering techniques to enhance user experience in digital streaming platforms. By leveraging machine learning algorithms such as K-Nearest Neighbours (KNN) and Singular Value Decomposition (SVD), the system analyses user preferences and interactions to generate personalized movie recommendations. The backend of the system is built using Flask, while the frontend is developed with HTML, CSS, and JavaScript, ensuring an intuitive and responsive user interface. Despite its effectiveness, the collaborative filtering approach faces challenges such as data sparsity and the cold start problem, which can hinder recommendation accuracy. This paper discusses the evaluation metrics employed, including Mean Squared Error (MSE) and Precision@K, to assess the performance of the system. It also highlights the potential for future improvements, such as integrating content-based filtering and hybrid models to enhance the adaptability and precision of the recommendations. By optimizing content discovery, the system aims to improve user engagement and satisfaction in the rapidly growing digital streaming market. Keywords: Movie Recommendation System, Collaborative Filtering, KNN, SVD, Flask, Personalization, Machine Learning, Streaming Platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".