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Found 12 papers, 2 papers with code
Farzi Data: Autoregressive Data Distillation
We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure.
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval.
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings.
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
It is also very encouraging that our framework further improves head items and overall performance on top of the gains on tail items.
Learning to Embed Categorical Features without Embedding Tables for Recommendation
Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
Self-supervised Learning for Large-scale Item Recommendations
Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys).
Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution.
