Search Results for author:
Found 8 papers, 3 papers with code
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example.
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Recommender systems play an important role in many content platforms.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods.
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.
Recommending what video to watch next: a multitask ranking system
In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.
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.
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
We showcase how we use them as a replacement of item IDs in a resource-constrained ranking model used in an industrial-scale video sharing platform.
Aligning Large Language Models with Recommendation Knowledge
Operations such as Masked Item Modeling (MIM) and Bayesian Personalized Ranking (BPR) have found success in conventional recommender systems.
