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Neural Collaborative Filtering: Learning Non-Linear User-Item Preferences

Watch a real Neural Collaborative Filtering model train live: user and movie embeddings self-organize in 3D as backpropagation through a small MLP learns who likes what, replacing the plain dot-product of matrix factorization with a learned non-linear interaction function.

AI & Machine Learning3DAdvanced60 FPS📱 Mobile-adapted⇄ 2D version

Most recommender demos stop at a dot product. This one trains a real, small Neural Collaborative Filtering model in your browser — five viewers and six movies, each embedded as a point in 3D, feeding into a hidden layer that learns a non-linear scoring function via backpropagation. Press Train and watch full-batch gradient descent reshape the embedding space epoch by epoch: viewers who share taste drift toward each other, movies drift toward the viewers who'd enjoy them, and the live loss and accuracy readouts track exactly how well the network has learned the underlying preference pattern. Pick a viewer to see the model's current recommendation scores drawn as live connecting lines.

⚙ Under the hood

Train a real Neural Collaborative Filtering model live in 3D: watch user and movie embeddings self-organize via backpropagation through a small MLP that learns a non-linear interaction function, replacing the plain dot product of classic matrix factorization.

recommender-systemsneural-networkscollaborative-filteringbackpropagationembeddingsmachine-learning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install