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Found 4 papers, 2 papers with code
Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
However, in order to protect user privacy and optimize utility, it is also crucial for LLMRec to intentionally forget specific user data, which is generally referred to as recommendation unlearning.
FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
Specifically, the masked data of one modality (i. e., tokens or features) has to be recovered with the help of the other modality, which establishes the feature-level interaction and alignment via sufficient mutual information extraction between dual modalities.
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
Traditional CTR models convert the multi-field categorical data into ID features via one-hot encoding, and extract the collaborative signals among features.
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing
Besides, the students' response records contain valuable relational information between questions and knowledge concepts.
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