Description: LLMRec: Large Language Models with Graph Augmentation for Recommendation
recommendation (500) large language models (60) data augmentation (11)
The problem of data sparsity has long plagued recommendation systems, and previous studies attempted to mitigate this issue by incorporating side information. However, this approach inevitably introduces side effects such as noise, availability issues, and quality issues. These problems hinder the effective modeling of user preferences and impact recommendation performance. In light of the advancements in large language models (LLMs) that possess a wide knowledge base and notable reasoning capabilities, we
Figure 1: The LLMRec framework: (1) Three types of data augmentation strategies: i) augmenting user-item interactions; ii) enhancing item attributes, and iii) user profiling. (2) Augmented training with and denoised data robustification mechanism.
Figure 3: Constructed prompt for LLMs' completion including i) task description, ii) historical interactions, iii) candidates, and iv) output format description.