Search Results for author:
Found 7 papers, 3 papers with code
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
On this basis, we propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect.
Debiased Collaborative Filtering with Kernel-Based Causal Balancing
Inspired by these gaps, we propose to approximate the balancing functions in reproducing kernel Hilbert space and demonstrate that, based on the universal property and representer theorem of kernel functions, the causal balancing constraints can be better satisfied.
Pareto Invariant Representation Learning for Multimedia Recommendation
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder.
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety.
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance.
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities.
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning.
