Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recommendations. In this study we tackle the challenges of modeling long user histories for preference understanding in natural language. Specifically, we introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings, to use them as soft prompts to a LM. Our experiments demonstrate the superior capability of this approach in handling significantly longer histories compared to conventional text based prompting methods, yielding substantial improvements in predictive performance. The main contribution of this research is to demonstrate the ability to bias language models with user signals represented as embeddings.

翻译：对长历史行为进行建模在增强推荐系统中发挥着关键作用，能够捕捉用户不断变化的偏好，从而实现更精准、更个性化的推荐。在本研究中，我们旨在解决对用户长历史行为进行建模以理解自然语言偏好的挑战。具体而言，我们提出了一种新型用户嵌入模块（UEM），该模块通过将用户自由文本形式的交互历史压缩并表示为嵌入向量，从而高效地处理长历史数据，并将其作为软提示输入到语言模型中。实验表明，与传统的基于文本的提示方法相比，本方法在处理显著更长的历史数据方面具有卓越能力，并显著提升了预测性能。本研究的主要贡献在于证明了利用以嵌入向量表示的用户信号来影响语言模型的能力。