A 1-by-1 convolutional autoencoder with six-way softmax outputs jointly predicts interaction likelihood and conditional rating, supported by generalization bounds and mixed but mostly competitive RMSE and Recall results on five recommendation datasets.
Knowledge-guided article embedding refinement for session-based news recommendation,
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Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
A 1-by-1 convolutional autoencoder with six-way softmax outputs jointly predicts interaction likelihood and conditional rating, supported by generalization bounds and mixed but mostly competitive RMSE and Recall results on five recommendation datasets.