The Toronto-Dominion Bank
TWO-HEADED ATTENTION FUSED AUTOENCODER FOR CONTEXT-AWARE RECOMMENDATION

Abstract:

A recommendation system uses a trained two-headed attention fused autoencoder to generate likelihood scores indicating a likelihood that a user will interact with a content item if that content item is suggested or otherwise presented to the user. The autoencoder is trained to jointly learn features from two sets of training data, including user review data and implicit feedback data. One or more fusion stages generate a set of fused feature representations that include aggregated information from both the user reviews and user preferences. The fused feature representations are inputted into a preference decoder for making predictions by generating a set of likelihood scores. The system may train the autoencoder by including an additional NCE decoder that further helps with reducing popularity bias. The trained parameters are stored and used in a deployment process for making predictions, where only the reconstruction results from the preference decoder are used as predictions.