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Found 2 papers, 1 papers with code
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback.
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
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