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Found 2 papers, 1 papers with code
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.
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.
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