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

Authors

  • Debora Patricia Sharon Rembet Department of Informatics, Faculty of Information Technology, Universitas Kristen Duta Wacana, Yogyakarta, Indonesia
  • Antonius Rachmat Chrismanto Department of Informatics, Faculty of Information Technology, Universitas Kristen Duta Wacana, Yogyakarta, Indonesia
  • Lucia Dwi Krisnawati Department of Informatics, Faculty of Information Technology, Universitas Kristen Duta Wacana, Yogyakarta, Indonesia

DOI:

https://doi.org/10.58776/ijitcsa.v4i3.269

Keywords:

Hybrid Filtering, Collaborative Filtering, Content-Based Filtering, Personal Threshold

Abstract

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.

Keywords—Hybrid Filtering, Collaborative Filtering, Content-Based Filtering, Personal Threshold

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Published

26-10-2026

How to Cite

Rembet, D. P. S., Chrismanto, A. R., & Krisnawati, L. D. (2026). Hybrid Filtering Implementation with Personal Threshold Mechanism to Improve Movie Recommendation Accuracy. International Journal of Information Technology and Computer Science Applications, 4(3), 235–245. https://doi.org/10.58776/ijitcsa.v4i3.269

Issue

Vol. 4 No. 3 (2026): September - December 2026

Section

New Submission

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Copyright (c) 2026 Debora Patricia Sharon Rembet, Antonius Rachmat Chrismanto, Lucia Dwi Krisnawati

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