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Visually-Aware Fashion Recommendation and Design with Generative Image Models
Wang-Cheng Kang, Chen Fang, Zhaowen Wang +1
Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fas…
Does mitigating ML's impact disparity require treatment disparity?
Zachary C. Lipton, Alexandra Chouldechova, Julian McAuley
Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making. Algorithms exhibit treatment disparity i…
SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation
Chenwei Cai, Ruining He, Julian McAuley
Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in…
Translation-based Recommendation
Ruining He, Wang-Cheng Kang, Julian McAuley
Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is p…
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
Chris Donahue, Zachary C. Lipton, Akshay Balsubramani +1
We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (e.g. individual humans) and observations (e.g. specifi…
Deduplication in a massive clinical note dataset
Sanjeev Shenoy, Tsung-Ting Kuo, Rodney Gabriel +2
Duplication, whether exact or partial, is a common issue in many datasets. In clinical notes data, duplication (and near duplication) can arise for many reasons, such as the pervas…