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Found 6 papers, 5 papers with code
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers.
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Recently, the embedding-based recommendation models (e. g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility.
Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs
Then, the SCG can be trained based on these surrogate costs using standard backpropagation.
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Video relevance prediction is one of the most important tasks for online streaming service.
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE).
A Neural Autoregressive Approach to Collaborative Filtering
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE).
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