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Found 5 papers, 4 papers with code
InstructAgent: Building User Controllable Recommender via LLM Agent
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms.
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation
We also propose a Generative Recommendation Framework combined with three regularizers inspired by the mutual information maximization (MIM) theory \cite{mcgill1954multivariate} to capture the semantic differences between a user's interests shared across domains and those specific to certain domains, as well as address the informational gap between a user's actual interaction sequences and the pseudo-sequences generated.
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Motivated by this insight, we empower small language models for SR, namely SLMRec, which adopt a simple yet effective knowledge distillation method.
Masked Face Dataset Generation and Masked Face Recognition
In the post-pandemic era, wearing face masks has posed great challenge to the ordinary face recognition.
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.
