AI systems that model and interact with users can update their models over time to reflect new information and changes in the environment. Although these updates can improve the performance of the AI system, they may actually hurt the performance for individual users. Prior work has studied the trade-off between improving the system  accuracy following an update and the compatibility of the update with prior user experience. The more the model is forced to be compatible with prior updates , the higher loss in accuracy it will incur. In this paper, we show that  in some cases it is possible to improve this compatibility-accuracy trade-off relative to a specific user by employing new error functions for the AI updates that personalize the weight updates to be compatible with the user's history of interaction with the system and present experimental results indicating that this approach provides major improvements to certain users .
AI systems that model and interact with users can update their models over time to reflect new information and changes in the environment. Although these updates may improve the overall performance of the AI system, they may actually hurt the performance with respect to individual users. Prior work has studied the trade-off between improving the system 's accuracy following an update and the compatibility of the updated system with prior user experience. The more the model is forced to be compatible with a prior version , the higher loss in accuracy it will incur. In this paper, we show that by personalizing the loss function to specific users, in some cases it is possible to improve the compatibility-accuracy trade-off with respect to these users (increase the compatibility of the model while sacrificing less accuracy). We present experimental results indicating that this approach provides moderate improvements on average (around 20\%) but large improvements for certain users (up to 300\%) .
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relative to a specific user by employing new error functions for the AI updates that personalize the weight updates to be compatible with the user's history of interaction with the system and
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major improvements to certain users
moderate improvements on average (around 20\%) but large improvements for certain users (up to 300\%)