205 citations · 352 across the 8 of their papers we have counts for
8 papers
Aligning Large Language Models with Recommendation Knowledge
Yuwei Cao, Nikhil Mehta, Xinyang Yi +5
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like…
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…
Regularization for Matrix Completion
Raghunandan H. Keshavan, Andrea Montanari
We consider the problem of reconstructing a low rank matrix from noisy observations of a subset of its entries. This task has applications in statistical learning, computer vision,…
Low-rank Matrix Completion with Noisy Observations: a Quantitative Comparison
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
We consider a problem of significant practical importance, namely, the reconstruction of a low-rank data matrix from a small subset of its entries. This problem appears in many are…
Matrix Completion from Noisy Entries
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries. The problem arises in a variety of appl…
Matrix Completion from a Few Entries
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
Let M be a random (alpha n) x n matrix of rank r<<n, and assume that a uniformly random subset E of its entries is observed. We describe an efficient algorithm that reconstructs M…
Learning Low Rank Matrices from O(n) Entries
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh
How many random entries of an n by m, rank r matrix are necessary to reconstruct the matrix within an accuracy d? We address this question in the case of a random matrix with bound…