<p>Based on Kula's [paper](<a href="https://arxiv.org/pdf/1507.08439.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1507.08439.pdf</a>, LightFM starts from a CF matrix factorization algorithm while also learning both users and items embeddings in the process (if such data available). However, if no user/item features are provided to the model, its behaviour will be that of a MF (Matrix Factorization) method.</p>
Standard Collaborative Filtering vs Hybrid Light FM
<p>I'm new to recommender system and trying to understand the fundamental difference between standard collaborative filtering (CF) and hybrid methods like LightFM. As I researched online, most of the posts mentioned hybrid method combines both CF and content-based method. But from a matrix/math standpoint, LightFM also learns item-user interaction embedding, like CF. How are they different?</p>

<p>Thank you so much in advance.</p>