Content-based filtering recommends titles by their attributes: genre, cast, mood, themes, and other metadata. If you watched and liked a title, it suggests others that are similar in these features, regardless of what other users did. It leans heavily on rich, accurate metadata.
Its advantage is that it works without a crowd - it can recommend a brand-new title the moment it has metadata, easing the cold-start problem that hobbles collaborative filtering. Its limitation is that it tends toward 'more of the same,' missing the surprising cross-genre hits that behavioral signals catch. That complementarity is why real recommenders blend content-based and collaborative approaches.
The handbook · 7 volumes
Fora Soft Video Engineering Handbook
Content-based filtering recommends titles that look and sound like what a viewer already watched, so rich metadata matters more than traffic. We wrote seven volumes covering the video stack; for content-based filtering start with Volume 6 on computer vision, ASR, and search over a video archive, then Volume 7 on serving and cost. Written by Nikolay Sapunov, CEO at Fora Soft.
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