Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization (2025)

Abstract: Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual’s historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code at https://github.com/SnowCharmQ/DPL .

## introduction with continuous efforts, large language models (llms) @xcite @xcite have demonstrated increasingly higher levels of intelligence, sparking an unprecedented enthusiasm for applying them to individuals' daily lives to enhance personal thinking, planning, and overall life experiences @xcite @xcite . however, the "onesize-fits-all" paradigm of general llms becomes ineffective when serving individual users, as users have distinct preferences and would expect personalized generation from llms (christakopoulou * equal contribution † corresponding author @xcite . this motivates an exciting new direction: llm personalization @xcite @xcite -adapting llms to generate text responses that align with each individual's unique preferences. llm personalization fundamentally relies on incorporating user-specific information into llms to tailor their generation @xcite . the "memorize-then-inject" framework offers a promising and economical solution -storing users' historical data and then extracting key information to serve as instructional contexts for customizing llm responses @xcite @xcite . depending on the extraction method employed, existing approaches can generally be categorized into: 1) retrieval-based methods @xcite , which just retrieve the parts of the user's history relevant to the current request for llm use, and 2) summarization-based methods @xcite , which further condense the user history to distill preferences for llm use. while these methods @xcite have made notable progress, we contend that they share a common limitation: they overlook the inter-user comparative analysis, which is essential for identifying the differences that truly shape preferences. as acknowledged in psychology and behavioral science @xcite @xcite , it is the differences among individuals that make them unique, while their uniqueness shapes their preferences. this underscores that effective personalization hinges on identifying and understanding these differences. as a result, without considering inter-user comparisons to extract differences, current methods would be suboptimal for personalization. in this work, we propose difference-aware user modeling -extracting the differences among users as key preference information -to enhance llm personalization. to achieve this, distinguishing each user from others and then summarizing the differences (using an llm) to serve as the instructional contexts for personalization provides an intuitive solution. however, it faces two challenges: 1) comparing to all users may be impractical due to the high cost and complexity of extracting differences across all others; and 2) without proper guidance, the differences extracted by the llm may be irrelevant or meaningless for the task. therefore, we must address: 1) how to select appropriate users for comparison, and 2) how to extract task-relevant differences during comparison effectively. to address these challenges, we introduce a novel difference-aware personalization learning (dpl) method. dpl selectively chooses representative users for comparison and establishes an extraction standard to effectively capture task-relevant differences, using the extracted differences as the llm's instructional contexts for personalization. specifically, we cluster users based on data similarity and select only cluster-center users for comparison, ensuring efficiency while maintaining a global perspective. to extract task-relevant differences, we structure the difference extraction model (an llm) to focus on key predefined dimensions for capturing differences, rather than operating freely. three key preference dimensions are distilled from existing literature as examples: writing style, emotional tone, and semantic content. we validate our dpl method on a representative personalized text generation task, namely, review generation @xcite @xcite . extensive results demonstrate that dpl effectively enhances llm personalization. the main contributions of this work are summarized as follows: ## preliminary problem formulation. this work focuses on personalizing llms to generate text outputs that align with user preferences, i.e., achieving personalized text generation. we assume that users have a set of historical texts, either written or preferred by them, reflecting the users' preferences. we could leverage this data to customize llm's generation to meet user needs. formally, let d denote the historical data for all users. each sample in d is represented as (u, i, y) ∈ d, where u represents the user, i denotes the item (or object) the user is focused on, and y indicates the text written or preferred by u for item i. when a target user u ′ submits a new request for text generation on a target item i ′ , the llm needs to generate text that satisfies the user's preferences based on d. notably, without loss of generality, this paper primarily focuses on review generation, a representative personalized text generation task. in this context, the goal is to enable llms to generate personalized reviews for items such as movies or products, ensuring alignment with true user reviews. ## methodology in this section, we introduce the proposed difference-aware personalization learning (dpl) method, beginning with its motivation and the overall framework, followed by a detailed explanation of each component. ## experiments in this section, we conduct experiments to answer the following research questions: rq1: how does dpl perform on the personalized text generation task with real-world datasets, in comparison to other baseline methods? rq2: what is the impact of the individual components of dpl on its effectiveness? rq3: how do the specific hyper-parameters of dpl influence its performance? rq4: how does dpl perform across different levels of user uniqueness? rq5: how does dpl qualitatively demonstrate its personalized generation capabilities through concrete case analysis? ## conclusions this study introduced dpl, a novel method that enhances llm personalization by identifying and leveraging inter-user differences as key preference information. we proposed a selective user comparison mechanism and a structured difference extraction method to extract task-relevant meaningful differences effectively. experimental results on the personalized review generation task show that dpl significantly enhances llm personalization. currently, we only focus on review generation tasks to validate our method. in future, we plan to extend it to other tasks for broader verification. additionally, our method's effectiveness depends on the longcontext processing capabilities of llms, and we currently assume that the context window is sufficient for handling our data. we also plan to extend our method to address long-context cases.