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Found 2 papers, 1 papers with code
Debiased Model-based Interactive Recommendation
Existing model-based interactive recommendation systems are trained by querying a world model to capture the user preference, but learning the world model from historical logged data will easily suffer from bias issues such as popularity bias and sampling bias.
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations
To achieve this, we firstly formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences.
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