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Found 9 papers, 4 papers with code
Debiased Recommendation with Noisy Feedback
Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate.
Phased Instruction Fine-Tuning for Large Language Models
Instruction Fine-Tuning enhances pre-trained language models from basic next-word prediction to complex instruction-following.
Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions
The overarching goal is to furnish researchers with a pragmatic guide for effective LLM evaluation and metric selection, thereby advancing the understanding and application of these large language models.
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.
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.
Multiple Robust Learning for Recommendation
Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
Prevent the Language Model from being Overconfident in Neural Machine Translation
The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation.
