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GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,...
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iCaRL: Incremental Classifier and Representation Learning
A major open problem on the road to artificial intelligence is the devel...
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Imbalanced Deep Learning by Minority Class Incremental Rectification
Model learning from class imbalanced training data is a long-standing an...
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Incremental Few-Shot Object Detection
Most existing object detection methods rely on the availability of abund...
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Collaborative Method for Incremental Learning on Classification and Generation
Although well-trained deep neural networks have shown remarkable perform...
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Lambda Learner: Fast Incremental Learning on Data Streams
One of the most well-established applications of machine learning is in ...
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MaskIt: Masking for efficient utilization of incomplete public datasets for training deep learning models
A major challenge in training deep learning models is the lack of high q...
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A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data. Such batch mode training of deep learning models suffers from low training efficiency, which may lead to performance degradation when the model is not produced on time. To tackle this issue, incremental learning is proposed and has received much attention recently. Incremental learning has great potential in recommender systems, as two consecutive window of training data overlap most of the volume. It aims to update the model incrementally with only the newly incoming samples from the timestamp when the model is updated last time, which is much more efficient than the batch mode training. However, most of the incremental learning methods focus on the research area of image recognition where new tasks or classes are learned over time. In this work, we introduce a practical incremental method to train deep CTR models, which consists of three decoupled modules (namely, data, feature and model module). Our method can achieve comparable performance to the conventional batch mode training with much better training efficiency. We conduct extensive experiments on a public benchmark and a private dataset to demonstrate the effectiveness of our proposed method.
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