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Found 7 papers, 3 papers with code
Density Weighting for Multi-Interest Personalized Recommendation
When the data distribution is highly skewed, the gains observed by learning multiple representations diminish since the model dominates on head items/interests, leading to poor performance on tail items.
Nonlinear Initialization Methods for Low-Rank Neural Networks
We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-rank matrices.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Learning effective feature crosses is the key behind building recommender systems.
Ranked #12 on
Click-Through Rate Prediction
on Criteo
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.
Top-K Off-Policy Correction for a REINFORCE Recommender System
The contributions of the paper are: (1) scaling REINFORCE to a production recommender system with an action space on the orders of millions; (2) applying off-policy correction to address data biases in learning from logged feedback collected from multiple behavior policies; (3) proposing a novel top-K off-policy correction to account for our policy recommending multiple items at a time; (4) showcasing the value of exploration.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval.
