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This survey defines model fusion and organizes prior work into three levels: parameter-level, representation-level, and behavior-level fusion, which provides a clear map of this area and support future work on model fusion.
By dynamically weighting historical interactions, TIPS lets sequential recommenders see past the biases of what users *actually* clicked, revealing what they *would* have clicked.