Guided Profile Generation Improves Personalization with Large Language Models (2024)

Abstract: In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLM). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual’s unique habits and preferences. Our experimental results show that GPG improves LLM’s personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.

## introduction within the context of personalization tasks, personal profiling has been extensively employed. conventional methodologies typically rely on substantial datasets such as graph-based similarities. these profiles often exhibit 'neighborhoods' and 'relationships' within the data, posing challenges for immediate interpretability without supplementary processing. recently, llms have demonstrated robust capabilities in tasks related to reasoning and generation, leading to a growing interest in leveraging llms for personalization services. however, distinguished from other naturual language processing (nlp) tasks, we identify two primary challenges in personalization with llms. the first challenge is the complexity of personal contexts and the sparsity of their key information. for example, a person's distinctive writing style may only be discernible in a small portion of their writing, whereas the remainder of the writing style tends to be more generic. as is shown in recent studies @xcite , llms have challenges in capturing comprehensive information within lengthy contexts, making it easy to overlook the smaller portions that contain distinctive writing styles. previous studies @xcite have attempted to address this challenge by context retrieval. however, context retrievers frequently rely on surface-level ranking strategies, such as keyword similarity. such an approach, while straightforward, may not always align with the nuanced needs of personalization tasks. the second challenge lies in the balance between generalization and personalization. while llms have demonstrated considerable performance on general tasks, they still struggle to generate output that fully aligns with users' desired behaviors and directions @xcite . rather, they prioritize imitating the majority of their training sets @xcite . figure 1 illustrates a personalized task involving the paraphrasing of a tweet to match someone's distinctive writing style. from the personal context, it is noticeable that the individual tends to use block letters to emphasize actions and feelings. however, the model closely mirrors the original question input when receiving the personal context and question directly, which can be reachable even without personal context. when we instruct llm to describe the person's writing style, rather than noticing the spatial use of capitalization, it pays attention to the emotion, and content, which are not our desired 'writing styles'. steering llm outputs precisely is always a challenge. to address it, previous work has attempted to apply reinforcement learning from human feed- ## related work llms have demonstrated robust performance through scaling up, in-context learning @xcite , reinforcement learning from human feedback @xcite , and instruction tuning @xcite , making them capable of complex reasoning tasks @xcite @xcite . the performance of the model is sensitive to input and output manners, making prompt optimization @xcite @xcite ) a popular topic. there has been a growing interest in using llms for personalization. llm-rec @xcite utilizes llms as recommenders by prompting them with recommendation instructions and employing graph-based engagements. however, this approach lacks emphasis on the crafting of user profiles. lamp @xcite attempts to integrate a context retriever to avoid the need for feeding the entire personal context to llms, but the retrieved personal context still proves challenging for llms to easily comprehend. palr @xcite uses llms to generate user profiles for personalized recommendation and fine-tuned llama @xcite to generate ranking. however, the exploration of more effective methods for crafting user profiles in natural language based on personal contexts with diverse structures remains underexplored. other studies also explore the use of llms to augment graph-based recommendation system @xcite , support human writing creativity @xcite , personalized writing education @xcite , dialogue systems @xcite and healthcare assistant @xcite . for datasets, lamp @xcite ) introduces seven language tasks that necessitate personalization. these tasks include tweet paraphrasing and email subject generation, among others. notably, tweet paraphrasing serves as a comprehensive test bed for evaluating personalized writing style imitation using llms. amazon review (he and mcauley, 2016) provides abundant online purchase history and shopping reviews, enabling the creation of a preference prediction dataset for product purchasing. per-chat @xcite is an open-domain single-turn dialogue dataset collected from reddit. in per-chat, each dialogue response is paired with related comment history from the same user, enabling personal profile crafting. other datasets like movielens @xcite , recipe @xcite , persona-chat @xcite are also widely used. we evaluate gpg by personalized preference prediction, tweet paraphrasing, and dialogue generation sets in this paper. ## guided profile generation given a personal context pc, and a task t, the objective of personalization is to align with the individual's behavior and successfully accomplish the task. in contemporary commercial systems, personal profile crafting proves advantages for both accuracy and efficiency, achieved by providing a clear reflection of a person's behavior and ensuring reusability without the need to process the raw context again. given the impressive capabilities of llms, there is a natural inclination to leverage them for integrating raw pc and generating personal profiles. however, our early investigation indicates that these approaches may not achieve the expected performance (figure 1 ). moreover, the lack of human-annotated data for intermediate personal profiles makes direct optimization through fine-tuning a challenging option. we propose gpg, a general method for personalization with llms through personal profile generation. the proposed method of gpg is presented in figure 2 . different from joint learning with downstream personalization tasks for llm, which adopts reinforcement learning from human feedback (rlhf), we adopt a much more cost-effective yet efficient method. this method focuses on gener- ## evaluation tasks and metrics our proposed method can be applied to a wide range of personalization tasks to overcome the challenge given by raw personal contexts. in this work, we mainly focus on the task of personalized preference prediction, text paraphrasing, and dialogue continuation. ## experiments we use openai's gpt-3.5-turbo-1106 as our major llm all through the tasks; during inference, we keep the temperature at 0 (greedy decoding) to gain a deterministic result and set max_tokens to 100. we report the result with a single run due to the greedy decoding. ## conclusion in this work, we present guided profile generation gpg, a novel method leveraging llms for personalization tasks through profile generation and context digestion. we conduct extensive experiments on various personalization tasks, including preference prediction, text paraphrasing, and dialogue continuation. despite the superior performance, gpg generates a personal profile in pure natural descriptive language, which is interpretable and easily diagnosable. moreover, we reveal why and how the guidance and descriptive personal profile improve the performance. we hope our research can pave the way for personalization applications with ai models in the future. in figure 4 , we present examples of the three tasks under our test, we include raw personal context, personal context digestion, and personal profile in each example. the prompts for generating personal context digestion and personal profiles can be found in section 5.2.