Large Language Models (LLMs) excel at tackling various natural language tasks. However, due to the significant costs involved in re-training or fine-tuning them, they remain largely static and difficult to personalize. Nevertheless, a variety of applications could benefit from generations that are tailored to users' preferences, goals, and knowledge. Among them is web search, where knowing what a user is trying to accomplish, what they care about, and what they know can lead to improved search experiences. In this work, we propose a novel and general approach that augments an LLM with relevant context from users' interaction histories with a search engine in order to personalize its outputs. Specifically, we construct an entity-centric knowledge store for each user based on their search and browsing activities on the web, which is then leveraged to provide contextually relevant LLM prompt augmentations. This knowledge store is light-weight, since it only produces user-specific aggregate projections of interests and knowledge onto public knowledge graphs, and leverages existing search log infrastructure, thereby mitigating the privacy, compliance, and scalability concerns associated with building deep user profiles for personalization. We then validate our approach on the task of contextual query suggestion, which requires understanding not only the user's current search context but also what they historically know and care about. Through a number of experiments based on human evaluation, we show that our approach is significantly better than several other LLM-powered baselines, generating query suggestions that are contextually more relevant, personalized, and useful.

翻译：大语言模型（LLMs）在处理各类自然语言任务方面表现出色。然而，由于重新训练或微调这些模型涉及高昂成本，它们基本保持静态且难以个性化。尽管如此，许多应用仍能从针对用户偏好、目标和知识定制的生成内容中受益。其中便包括网络搜索——了解用户试图完成的任务、关注点及已有知识，可显著改善搜索体验。本研究提出一种新颖且通用的方法，通过增强LLM与用户与搜索引擎交互历史的相关上下文信息，实现输出结果的个性化。具体而言，我们基于用户在网页上的搜索和浏览行为，为每位用户构建以实体为中心的知识存储库，并据此生成上下文相关的LLM提示增强。该知识库轻量高效——仅将用户特有的兴趣与知识聚合投射到公开知识图谱上，且复用现有搜索日志基础设施，从而缓解了构建深度用户画像时涉及的隐私、合规及可扩展性问题。我们以上下文查询建议任务验证该方法，该任务要求不仅理解用户当前搜索上下文，还需掌握其历史知识背景与关注重点。通过基于人工评估的多项实验表明，我们的方法显著优于多个基于LLM的基线模型，生成的查询建议在上下文相关性、个性化程度及实用性方面均表现更优。