Enhancing Recommendation Diversity by Re-ranking with Large Language
  Models
Diego Carraro Derek Bridge
It has long been recognized that it is not enough for a Recommender System
(RS) to provide recommendations based only on their relevance to users. Among
many other criteria, the set of recommendations may need to be diverse in order
to handle uncertainty and offer a meaningful choice. The literature reports
many ways of measuring diversity and ways of improving the diversity of a set
of recommendations, most notably by re-ranking and selecting from a larger set
of candidate recommendations. Driven by promising insights from the literature
on how to incorporate versatile Large Language Models (LLMs) into the RS
pipeline, in this paper, we show how LLMs can be used for diversity re-ranking.
  We begin with an informal study that verifies that LLMs can be used for
re-ranking tasks and do have some understanding of the concept of diversity.
Then, we design a more rigorous methodology where LLMs are prompted to generate
a diverse ranking from a candidate ranking using various prompt templates with
different re-ranking instructions in a zero-shot fashion. We conduct
comprehensive experiments testing state-of-the-art conversational LLMs from the
GPT and Llama families. We compare their re-ranking capabilities with random
re-ranking and various traditional re-ranking methods from the literature (MMR,
xQuAD and RxQuAD). We find that LLM-based re-ranking outperforms random
re-ranking across all the metrics that we use but does not perform as well as
the traditional re-ranking methods. We gain insight into prompt design for this
task (e.g.\ on the whole, it is better to prompt for diversity rather than a
balance of diversity and relevance). Given that no special knowledge
engineering is needed, we conclude that LLM-based re-ranking is a promising
approach, and we highlight directions for future research. We open-source the
code of our experiments for reproducibility.