567 citations · 1.1k across the 24 of their papers we have counts for
25 papers
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication
Zhe Zhao, Qingyun Liu, Huan Gui +3
Many recent breakthroughs in machine learning have been enabled by the pre-trained foundation models. By scaling up model parameters, training data, and computation resources, foun…
Density Weighting for Multi-Interest Personalized Recommendation
Nikhil Mehta, Anima Singh, Xinyang Yi +3
Using multiple user representations (MUR) to model user behavior instead of a single user representation (SUR) has been shown to improve personalization in recommendation systems.…
Online Matching: A Real-time Bandit System for Large-scale Recommendations
Xinyang Yi, Shao-Chuan Wang, Ruining He +6
The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. Whi…
Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
Anima Singh, Trung Vu, Nikhil Mehta +9
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, caus…
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi +6
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can parti…
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta +4
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can compre…