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06/29/2020
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is one of the most important machine...
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12/18/2019
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Recently, the embedding-based recommendation models (e.g., matrix factor...
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07/25/2018
Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs
In real-world scenarios, it is appealing to learn a model carrying out s...
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06/03/2018
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Video relevance prediction is one of the most important tasks for online...
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09/15/2017
Improving the Diversity of Top-N Recommendation via Determinantal Point Process
Recommender systems take the key responsibility to help users discover i...
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11/16/2016
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Clustering is among the most fundamental tasks in computer vision and ma...
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05/31/2016