Addressing unmeasured confounder for recommendation with sensitivity analysis
Recommender systems should answer the intervention question" if recommending an item
to a user, what would the feedback be", calling for estimating the causal effect of a …
to a user, what would the feedback be", calling for estimating the causal effect of a …
Causal recommendation: Progresses and future directions
Data-driven recommender systems have demonstrated great success in various Web
applications owing to the extraordinary ability of machine learning models to recognize …
applications owing to the extraordinary ability of machine learning models to recognize …
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Accurate recommendation and reliable explanation are two key issues for modern
recommender systems. However, most recommendation benchmarks only concern the …
recommender systems. However, most recommendation benchmarks only concern the …