AI research project
SOLAR
An open implementation for aligning language-model recommenders with both relevance and serendipity objectives.
- Year
- 2025
- Status
- active
- Focus
- Python · LLM Alignment · Recommendation
What it does
SOLAR targets recommendation results that are useful and pleasantly unexpected. It first trains an ID-based model against accuracy and serendipity objectives, then uses LLM reranking to expand the available supervision.
The final stage converts the resulting signals into recommendation-oriented instructions for language-model fine-tuning. The repository provides the official code and released data.
Evidence and scope
This page is a concise guide to the work. Use the primary paper for the experimental setup, quantitative results, limitations, and formal claims; use the repository for the implementation and current reproduction instructions.