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Found 5 papers, 3 papers with code
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation.
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors.
SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation
We further introduce a view-image consistency loss for the discriminator to emphasize the correspondence of the camera parameters and the images.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Instead of dismissing the role of incremental learning, we attribute the lack of anticipated performance enhancement to a mismatch between the LLM4Rec architecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendations, limiting its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context.
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
However, such a manner inevitably learns unstable feature interactions, i. e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving.
