REST-PG trains LLMs to reason over user profiles and self-train on high-reward outputs, improving personalized long-form generation by 14.5% over SFT on LongLaMP.
A Survey on Large Language Models for Personalized and Explainable Recommendations
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abstract
In recent years, Recommender Systems(RS) have witnessed a transformative shift with the advent of Large Language Models(LLMs) in the field of Natural Language Processing(NLP). These models such as OpenAI's GPT-3.5/4, Llama from Meta, have demonstrated unprecedented capabilities in understanding and generating human-like text. This has led to a paradigm shift in the realm of personalized and explainable recommendations, as LLMs offer a versatile toolset for processing vast amounts of textual data to enhance user experiences. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey aims to analyze how RS can benefit from LLM-based methodologies. Furthermore, we describe major challenges in Personalized Explanation Generating(PEG) tasks, which are cold-start problems, unfairness and bias problems in RS.
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Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation
REST-PG trains LLMs to reason over user profiles and self-train on high-reward outputs, improving personalized long-form generation by 14.5% over SFT on LongLaMP.