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Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up
The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained on the data of historical warm items.
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