  Reranking is a critical component in recommender systems, playing an
essential role in refining the output of recommendation algorithms. Traditional
reranking models have focused predominantly on accuracy, but modern
applications demand consideration of additional criteria such as diversity and
fairness. Existing reranking approaches often fail to harmonize these diverse
criteria effectively at the model level. Moreover, these models frequently
encounter challenges with scalability and personalization due to their
complexity and the varying significance of different reranking criteria in
diverse scenarios. In response, we introduce a comprehensive reranking
framework enhanced by LLM, designed to seamlessly integrate various reranking
criteria while maintaining scalability and facilitating personalized
recommendations. This framework employs a fully connected graph structure,
allowing the LLM to simultaneously consider multiple aspects such as accuracy,
diversity, and fairness through a coherent Chain-of-Thought (CoT) process. A
customizable input mechanism is also integrated, enabling the tuning of the
language model's focus to meet specific reranking needs. We validate our
approach using three popular public datasets, where our framework demonstrates
superior performance over existing state-of-the-art reranking models in
balancing multiple criteria. The code for this implementation is publicly
available.
