Enhancing Recommendation Diversity by Re-ranking with Large Language Models
作者: Diego Carraro, Derek Bridge
分类: cs.IR, cs.LG
发布日期: 2024-01-21 (更新: 2024-06-17)
备注: 32 pages, 2 figures
💡 一句话要点
利用大语言模型进行推荐系统多样性重排序
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 推荐系统 多样性重排序 大语言模型 自然语言处理 机器学习
📋 核心要点
- 现有推荐系统往往只关注推荐的相关性，忽视了推荐结果的多样性，导致用户选择的局限性。
- 本文提出利用大语言模型进行推荐结果的多样性重排序，通过零-shot方式生成多样化的推荐排名。
- 实验结果显示，LLM重排序方法在性能和成本上优于随机重排序，但仍低于传统重排序方法，未来有潜力提升。
📝 摘要（中文）
推荐系统不仅需要提供与用户相关的推荐，还需确保推荐的多样性。本文探讨了如何利用大语言模型（LLMs）进行推荐结果的多样性重排序。通过初步研究验证了LLMs在重排序任务中的有效性，并设计了严格的方法论，利用不同的提示模板生成多样化的排名。实验表明，LLM基于的重排序方法在性能和成本上优于随机重排序，但尚不及传统重排序方法。尽管如此，LLM方法展现出良好的前景，未来有望在推荐系统中更具竞争力。
🔬 方法详解
问题定义：本文解决推荐系统中推荐结果缺乏多样性的问题。现有方法主要关注相关性，导致用户选择的单一性和推荐的局限性。
核心思路：通过利用大语言模型（LLMs）进行重排序，生成多样化的推荐结果。设计了多种提示模板，以零-shot方式引导LLMs生成多样化排名。
技术框架：整体流程包括：首先从候选推荐中生成初步排名，然后通过LLMs进行重排序，最后输出多样化的推荐结果。主要模块包括候选生成、LLM重排序和结果输出。
关键创新：最重要的创新在于将LLMs引入推荐系统的重排序环节，利用其对多样性概念的理解，提升推荐结果的多样性。与传统方法相比，LLMs能够在更大范围内探索推荐选项。
关键设计：在实验中，使用了多种提示模板，设置了不同的重排序指令。通过对比实验，评估了LLM重排序的性能，关注了参数设置和模型选择的影响。实验代码已开源，便于复现。
🖼️ 关键图片
📊 实验亮点
实验结果表明，LLM基于的重排序方法在多样性提升方面优于随机重排序，尽管在性能上仍低于传统重排序方法。具体而言，LLM方法在自然语言处理和推荐任务中展现出更好的性能和较低的推理成本，预示着其未来的竞争力。
🎯 应用场景
该研究的潜在应用领域包括电商平台、内容推荐系统和社交媒体等，能够帮助用户在海量信息中做出更具意义的选择。通过提升推荐的多样性，增强用户体验，进而提高用户满意度和平台粘性。未来，随着LLMs技术的进一步发展，其在推荐系统中的应用将更加广泛和深入。
📄 摘要（原文）
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. Diversity is one way of handling recommendation uncertainty and ensuring that recommendations offer users a meaningful choice. The literature reports many ways of measuring diversity and 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 item 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 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. We open-source the code of our experiments for reproducibility. Our findings suggest that the trade-offs (in terms of performance and costs, among others) of LLM-based re-rankers are superior to those of random re-rankers but, as yet, inferior to the ones of traditional re-rankers. However, the LLM approach is promising. LLMs exhibit improved performance on many natural language processing and recommendation tasks and lower inference costs. Given these trends, we can expect LLM-based re-ranking to become more competitive soon.