Deep Models of Interactions Across Sets

ICML 2018 Jason HartfordDevon R GrahamKevin Leyton-BrownSiamak Ravanbakhsh

We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions. The canonical representation of such interactions is a matrix (or a higher-dimensional tensor) with an exchangeability property: the encoding's meaning is not changed by permuting rows or columns... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Collaborative Filtering Douban Factorized Exchangeable Autoencoder RMSE 0.738 # 4
Collaborative Filtering Flixster Factorized Exchangeable Autoencoder RMSE 0.908 # 1
Collaborative Filtering MovieLens 100K Self-Supervised Exchangeable Model RMSE 0.91 # 2
Collaborative Filtering YahooMusic Factorized Exchangeable Autoencoder RMSE 20.0 # 1