Comparative Analysis of LightFM and Neural Collaborative Filtering for Anime Recommendation Systems - Farras, Daffa Haidar; Rahmatulloh, Alam (Intelmatics, 2026)
The rapid growth of digital entertainment content such as movies and anime poses challenges in providing relevant viewing recommendations to users. Recommendation systems are a crucial solution to improve the user experience when exploring thousands of titles across various platforms. This study develops and compares two main approaches to recommendation systems: LightFM and the Collaborative Method . Classic Filtering (CF), as well as Neural Collaborative Deep- based Filtering (NCF) learning . Evaluation was conducted using Precision@K and Recall@K metrics . The test results showed that NCF was able to provide more relevant recommendations, with a Precision@5 value of 0.7983, much higher than LightFM which only reached 0.1721. Although LightFM showed a high AUC value (0.9134), its performance in generating Top-K recommendations was still low. Thus, it can be concluded that modern neural network -based approaches such as NCF are more effective than classical methods in the context of anime recommendation systems.