Hybrid Filtering Implementation with Personal Threshold Mechanism to Improve Movie Recommendation Accuracy

The growing volume of content on film streaming platforms makes it harder for users to find films that suit their tastes, highlighting the need for a personalized recommendation system. This research introduces a Hybrid Filtering architecture that unifies Collaborative Filtering using Singular Value Decomposition with Content-Based Filtering employing TF-IDF weighted vectors and cosine similarity measurement. The defining innovation is a dual-threshold mechanism grounded in each user's personal average rating (user_avg_rating), which individually filters candidate films before their hybrid scores are combined. New users are handled through a genre-based onboarding flow supported by a weighted popularity score. Experiments on the MovieLens ml-latest-small dataset (100,836 ratings, 610 users, 9,742 films) show that Hybrid Filtering achieves the best Precision@10 of 0.27 and NDCG@10 of 0.32. Switching from a fixed global threshold to the personal threshold raised Genre Precision from 0.40 to 0.90 (+125%). The SVD component attained RMSE of 0.88 and MAE of 0.67, and a user study with 10 participants yielded a mean relevance score of 8.2 out of 10.