User Embedding Model for Personalized Language Prompting

• URL: http://arxiv.org/abs/2401.04858v1
• Date: Wed, 10 Jan 2024 00:35:52 GMT
• Title: User Embedding Model for Personalized Language Prompting
• Authors: Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash, Sukhdeep Sodhi, Dima Kuzmin
• Abstract summary: We introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings. Our experiments demonstrate the superior capability of this approach in handling significantly longer histories. The main contribution of this research is to demonstrate the ability to bias language models with user signals represented as embeddings.
• Score: 9.472634942498859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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