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Found 4 papers, 2 papers with code
Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning
Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data.
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.
Slate-Aware Ranking for Recommendation
Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage.
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder
This paper attempts to tackle the item cold-start problem by generating enhanced warmed-up ID embeddings for cold items with historical data and limited interaction records.
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