3 papers
cs.IR2025
Identifying and Upweighting Power-Niche Users to Mitigate Popularity Bias in Recommendations
David Liu, Erik Weis, Moritz Laber +2
Recommender systems have been shown to exhibit popularity bias by over-recommending popular items and under-recommending relevant niche items. We seek to understand niche users in…
cs.LG2024
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings
David Liu, Arjun Seshadri, Tina Eliassi-Rad +1
A wide range of graph embedding objectives decompose into two components: one that enforces similarity, attracting the embeddings of nodes that are perceived as similar, and anothe…
cs.LG2023
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations
David Liu, Jackie Baek, Tina Eliassi-Rad
We study the fairness of dimensionality reduction methods for recommendations. We focus on the fundamental method of principal component analysis (PCA), which identifies latent com…