Qianqian Xu

Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size

2019 · IEEE Transactions on Knowledge and Data Engineering

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, …

Pareto Optimality for Fairness-constrained Collaborative Filtering

2021

The well-known collaborative filtering (CF) models typically optimize a single objective summed over all historical user-item interactions. Due to inevitable imbalances and biases in real-world data, they may develop a policy that unfairly discriminates against …

Geometry Interaction Knowledge Graph Embeddings

2022 · arXiv (Cornell University)

Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean space (zero curved), …

Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction

2022 · arXiv (Cornell University)

Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability …

A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization

2023 · arXiv (Cornell University)

Due to the inherent imbalance in real-world datasets, naïve Empirical Risk Minimization (ERM) tends to bias the learning process towards the majority classes, hindering generalization to minority classes. To rebalance the learning process, one straightforward …