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Found 5 papers, 3 papers with code
Aligning Language Models for Versatile Text-based Item Retrieval
Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks.
Ensemble linear interpolators: The role of ensembling
We introduce the multiplier-bootstrap-based bagged least square estimator, which can then be formulated as an average of the sketched least square estimators.
ConvFormer: Revisiting Transformer for Sequential User Modeling
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
How rotational invariance of common kernels prevents generalization in high dimensions
Kernel ridge regression is well-known to achieve minimax optimal rates in low-dimensional settings.
