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Found 6 papers, 4 papers with code
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Recommender systems play an important role in many content platforms.
Revisiting Adversarially Learned Injection Attacks Against Recommender Systems
Recommender systems play an important role in modern information and e-commerce applications.
Understanding and Improving Knowledge Distillation
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget.
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Our approach employs a mixture of models, each with a different temporal range.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'.
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
We propose a KD technique for learning to rank problems, called \emph{ranking distillation (RD)}.
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