87 citations · 118 across the 4 of their papers we have counts for
4 papers
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.…
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…
Recommender Systems with Generative Retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh +10
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search t…
Understanding and Improving Knowledge Distillation
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao +4
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, wher…