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Oct 30, 2023 · In this work, we propose a neural causal model to achieve counterfactual inference. Specifically, we first build a learnable structural causal ...
Oct 30, 2023 · Abstract—Survivor bias in observational data leads the opti- mization of recommender systems towards local optima. Cur-.
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A General Neural Causal Model for Interactive Recommendation ... Mitigation of the survivor bias is achieved though counterfactual consistency. ... Cannot find the ...
We provide an up-to-date collection and review of causal recommendation methods. □ All methods can be categorized into a causal-theoretically coherent taxonomy.
This concept paper contributes a summary of debiasing strategies in recommender systems and the design of several toy examples demonstrating the limits of these ...
Jan 26, 2024 · In this paper, we propose a knowledge-enhanced causal reinforcement learning model (KCRL) to mitigate data sparsity in IRSs. We make technical ...
Text S1 delves into the Related Fields, which clarifies the difference between causal inference for recommendation and other related causal techniques.
It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data ...
A knowledge-enhanced causal reinforcement learning model (KCRL) to mitigate data sparsity in IRSs is proposed and extensive experimental results on ...
Jan 13, 2024 · A Model-Agnostic Causal Learning Framework for Recommendation using Search Data · CAUSPref: Causal Preference Learning for Out-of-Distribution ...