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When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, Defu Lian, Enhong Chen

arXiv:2307.16376v1cs.IRcs.AIcs.CL

TL;DR

Personalization research has not fully addressed how to use emergent LLM capabilities, despite their potential to support more active interactions and personalized services. This perspective reviews existing challenges, LLM capabilities, and application pathways, while identifying interpretability, factuality, privacy, and evaluation concerns.

  • Problem

    Existing personalization research gives limited attention to the emergent capabilities of large language models, while applying them requires addressing domain-specific preferences, privacy, fairness, and incomplete evaluation resources.

  • Method

    The paper provides a perspective survey of personalization development and challenges, emergent LLM capabilities, and their uses from simple applications to tool-integrated personalization.

  • Results

    The paper identifies a progression from using LLM capabilities to enhance personalization performance toward integrating external tools for complex, end-to-end personalized tasks.

  • Takeaways & Limitations

    LLMs may support more interactive personalization and broader personalized services, including knowledge-base construction, planning, and tool-enabled task execution.

  • Takeaways & Limitations

    LLM personalization remains constrained by black-box behavior, potentially unfaithful explanations, misleading content, and risks involving user profiles and interaction histories.

Abstract

from arXiv · show

The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, and common-sense reasoning, etc. Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted. For one thing, it will reform the way of interaction between humans and personalization systems. Instead of being a passive medium of information filtering, large language models present the foundation for active user engagement. On top of such a new foundation, user requests can be proactively explored, and user's required information can be delivered in a natural and explainable way. For another thing, it will also considerably expand the scope of personalization, making it grow from the sole function of collecting personalized information to the compound function of providing personalized services. By leveraging large language models as general-purpose interface, the personalization systems may compile user requests into plans, calls the functions of external tools to execute the plans, and integrate the tools' outputs to complete the end-to-end personalization tasks. Today, large language models are still being developed, whereas the application in personalization is largely unexplored. Therefore, we consider it to be the right time to review the challenges in personalization and the opportunities to address them with LLMs. In particular, we dedicate this perspective paper to the discussion of the following aspects: the development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language models for personalization.

1 INTRODUCTION

The paper frames LLMs as reshaping personalization through active user engagement and broader personalized services. It reviews existing challenges, emergent LLM capabilities, and potential personalization applications.

  • Motivation: Personalization is presented as a bridge between people and machines that adapts digital experiences to individual preferences and needs.The paper emphasizes personalization’s role in improving user interactions and engagement.
  • Motivation: LLMs can transform personalization from passive information filtering into active exploration of user intents and needs.They support more natural, explainable delivery of requested information.
  • Research gap: Existing personalization research largely emphasizes traditional techniques or limited benchmarks, with less attention to emergent LLM capabilities.The paper positions its review as addressing this gap.
  • Scope: The survey covers personalization and LLM development, LLM applications ranging from simple capability use to tool integration, and adaptation challenges.Its organization follows this progression across subsequent sections.

2 BACKGROUND OVERVIEW

The background traces personalization from recommender systems and assistance toward conversational, interactive services enabled by LLM capabilities. It also highlights unresolved adaptation, privacy, fairness, and evaluation challenges.

  • Personalization systems: Recommender systems predict items of interest from users’ historical interactions and preferences, while personalization assistance tailors experiences using user characteristics and behavior.Conversational recommenders further elicit preferences through interactive dialogue.
  • Recommender systems: Traditional recommender systems evolved from collaborative filtering and matrix factorization toward deep models that capture sequential, temporal, contextual, and high-order feature interactions.Attention and graph-based networks are identified as representative techniques.
  • LLM capabilities: LLMs are scaled-up language models whose emergent abilities include in-context learning, instruction following, and step-by-step reasoning.Chain-of-Thought prompting can decompose complex tasks into sub-problems and intermediate answers.
  • LLMs for personalization: LLMs can support conversational search and recommendation by interpreting requests, explaining outputs, receiving feedback, and adjusting results.They may also act as assistants that remember information, make plans, and use external tools for task completion.
  • Open challenges: Applying LLMs to personalization remains challenging because user preferences are domain-specific, confidential information may be memorized, unfair predictions may occur, and evaluation resources are incomplete.The paper calls for systematic methodological and experimental frameworks.

3 LLMS FOR PERSONALIZATION

The paper examines LLM personalization through a progression from simple capability-based uses to complex integration with external tools and agent-like systems.

  • Application progression: The survey organizes LLM applications for personalization from using world knowledge and user-intent understanding to employing high-level reasoning and external tools.It also considers LLMs for enriching knowledge bases and acting as agents.

4 LLMS AS KNOWLEDGE BASE

The paper presents LLMs as a way to enrich recommender knowledge bases and construct knowledge graphs, while warning that factuality and incomplete knowledge remain constraints.

  • Knowledge-base foundations: Knowledge graphs represent entities as nodes and relations as edges, providing recommender systems with semantic side information.They help model relations between users and items.
  • Knowledge-base limitations: Existing recommender knowledge graphs are sparse and costly to build, with missing entities or relations that can omit user preferences and hurt recommendation performance.Construction requires data collection, integration, cleaning, and supervision.
  • LLMs as knowledge bases: LLMs can retrieve factual and commonsense knowledge and assist knowledge-graph construction through entity discovery, coreference resolution, relation extraction, and end-to-end generation.They can also distill knowledge into smaller models that generate graphs.
  • Potential benefits: LLM-based knowledge bases may become more extensive and up to date, support cross-domain recommendations, and improve recommendation accuracy, relevance, and personalization.The paper describes prompting LLMs for movie knowledge to enhance CTR prediction models.
  • Factuality risks: LLM knowledge can introduce ambiguity, inaccurate provenance, irrelevant information, or noise into recommendation processes.The cited benchmark passage reports GPT-4 at 0.012 Precision and 0.013 Recall on Named Entity Recognition, versus 0.712 and 0.706 for PL-Marker.

5 LLMS AS CONTENT INTERPRETER

Content interpreters have progressed from statistical and neural text encoders toward pretrained language models that capture richer semantics and world knowledge. LLM-based approaches extend recommendation beyond mainstream settings and may address sparse feedback and cold-start scenarios.

  • Motivation: Content-based recommendation uses item attributes to mitigate sparse feedback, but its content features can themselves be sparse.Relying only on clicks and browsing may not fully exploit item features.
  • Conventional Content Interpreters: Conventional content interpreters transform textual information into feature embeddings using statistical models, neural networks, and advanced NLP architectures.Examples include TF-IDF, bag-of-words, CNNs, RNNs, and attention models.
  • Pretrained Language Models: Pretrained language models such as BERT and GPT capture deep semantic representations and world knowledge, but require adaptation because their objectives misalign with recommendation tasks.Proposed adaptations include fine-tuning and recommendation-specific pretraining tasks such as masked opinion-token and opinion-rating prediction.
  • Broader Applications: Pretrained language models have been applied to tag, tweet, and code-example recommendation, while text-only inputs can alleviate cold-start problems and enable cross-domain recommendation.These applications extend language-model-based content interpretation beyond mainstream recommendation tasks.

6 LLMS AS EXPLAINER

Recommendation explanations have evolved from fixed templates toward natural-language generation, but conventional methods remain limited in adaptability, coherence, and generalizability. LLMs offer customized explanations and broader interpretational applicability, while introducing concerns about opacity, faithfulness, bias, and accuracy.

  • Motivation: Users seek comprehensible recommendation justifications because black-box recommenders can diminish trust.The paper illustrates this concern with drug recommendations whose effectiveness should be explainable.
  • Traditional Explanations: Traditional explainers use item-, user-, or attribute-based templates that convey essential information but may lack personalization and adaptability.Natural-language generation methods emerged partly to overcome these inflexible formats.
  • Limitations: Natural-language generation methods face long-range dependence problems and are often tightly coupled to particular recommendation models, limiting generalizability.Long inputs may produce explanations lacking diversity and coherence.
  • LLM-Based Explanations: LLMs can generate precise, natural, and adaptable explanations while using zero-shot, few-shot, and chain-of-thought prompting to incorporate feedback and support bidirectional alignment.The paper also connects LLM prompting with interpreting complex model behavior.
  • LLM-Based Explanations: LLM-based recommendation interpretations are presented as versatile and scalable because they are not bound to specific algorithms.This offers broader applicability than tightly coupled conventional explanation methods.
  • Open Issues: LLM explanations remain vulnerable to opacity, unfaithfulness, bias, erroneous content, and uneven task proficiency.The paper calls for monitoring and calibration to support fairness and accuracy.

7 LLMS AS COMMON SYSTEM REASONER

LLMs can support recommender systems through in-context learning, reasoning, and automated search, but direct zero/few-shot recommendation remains limited by domain coverage, evaluation requirements, and performance gaps.

  • Making Direct Recommendations: In-context learning enables LLMs to generate recommendations from natural-language instructions and demonstrations without task-specific tuning.The surveyed direct recommenders apply zero-shot or few-shot prompting to tasks including rating and ranking prediction.
  • Making Direct Recommendations: Zero/few-shot recommendation studies mainly use open-domain movie and book datasets, leaving private-domain performance insufficiently validated.The paper identifies e-commerce products and specific locations as challenging private-domain settings.
  • Making Direct Recommendations: Ranking evaluation often requires candidate-generation modules because generative LLMs produce item lists rather than retrieving from an existing candidate pool.These additional modules narrow the item pool before evaluating ordered recommendations.
  • Making Direct Recommendations: Few-shot learners outperform zero-shot recommenders, yet both remain substantially behind traditional and fine-tuned recommender models.The comparison specifically names fine-tuned LLM recommenders such as P5 and M6-Rec.
  • Making Direct Recommendations: Chain-of-thought prompting can improve recommendation performance by capturing preferences, extracting representative items, and reranking after filtering.The NIR strategy uses three prompting steps for these operations.
  • Reasoning for Automated Selection: LLM-assisted automated learning combines generative capabilities with search strategies, including genetic algorithms that use code-LLMs for mutation and crossover.This integration addresses the difficulty of making challenging technical decisions through prompting alone while retaining search guidance.
  • Reasoning for Automated Selection: Automated learning for recommender systems remains difficult because its search spaces are large, heterogeneous, and poorly understood, especially for high-order feature interactions.The paper emphasizes domain-specific components and the lack of strong knowledge about informative interactions.

8 LLMS AS CONVERSATIONAL AGENT

LLMs can serve conversational recommender systems by understanding intents, adapting to feedback, and supporting dialogue, but enterprise deployment faces private-data and long-context constraints.

  • Conversational Recommender Systems: Conversational recommender systems uncover preferences through dialogue and adjust recommendations in real time from user feedback.This distinguishes them from traditional systems that primarily rely on historical interactions.
  • Conversational Recommender Systems: Task-oriented conversational recommenders commonly separate dialogue understanding, state tracking, policy learning, and natural-language generation.This pipeline supports assistance with specific user tasks rather than open-domain chit-chat.
  • Conversational Recommender Systems: Conversational recommender approaches include attribute-based question answering, which decides whether to ask a question or provide a recommendation at each turn, and generative methods.The attribute-based approach uses a dialogue pipeline to guide turn-level decisions.
  • LLM-Based Dialogue: Instruction tuning and reinforcement learning from human feedback contributed to dialogue-capable models such as InstructGPT and ChatGPT.The passage also identifies BARD, LLaMA, and Vicuna as related large language dialogue models.
  • Challenges: Enterprise conversational recommenders lack reliable awareness of private-domain data because LLMs are primarily trained on publicly available internet sources.The paper discusses fine-tuning and tool learning as approaches under exploration for this challenge.
  • Challenges: Effective LLM tool use depends on carefully designed prompts, including chain-of-thought, Tree of Thoughts, Plan-and-Solve, and ReAct techniques.These methods guide models toward deeper reasoning and tool interaction.
  • Challenges: Long multi-turn conversations can exceed ChatGPT’s stated 4096-token input-output limit, making naive trimming of earlier dialogue risky for conversational recommendation.Discarding earlier turns can remove context needed to understand ongoing user preferences.

9 TOOL-LEARNING AND ITS APPLICATIONS IN REC-

Tool learning combines large language models with specialized tools to improve task solving and support personalization applications. In these systems, LLMs can interpret requests, select actions, use external information, and integrate results, while open challenges include better tool-use training and stronger recommendation engines.

  • Tool-learning paradigms: Tool learning combines specialized tools with foundation models to enhance task-solving capabilities.It includes tool-augmented learning, where tools assist AI, and tool-oriented learning, where models learn to use and control tools.
  • LLM-based tool learning: LLMs can decompose complex tasks, select and manage tools or models, and aggregate execution results.Examples include HuggingGPT, WebGPT, and ReAct, which connect language-based reasoning with external actions and feedback.
  • Applications in personalization: LLMs are appealing recommendation agents because they understand dialogue-based user intent, apply world knowledge, and communicate fluently.These abilities can support more seamless and personalized recommendation experiences.
  • Applications in personalization: Private-domain knowledge, item corpora, user profiles, and changing external information remain difficult for LLMs to memorize reliably.Without sufficient training or augmentation, these limitations can produce inaccurate responses and reduce behavioral control.
  • Applications in personalization: Search engines, recommendation engines, databases, and user-profile modules can augment LLMs with current, domain-specific, and user-specific information.Examples include retrieval and reranking, vector databases for new items, and profile tools for understanding user intent.
  • Future directions: Future work should improve tool use through fine-tuning and develop recommendation engines that exploit natural-language expressions of preference.In-context learning can struggle with complex contexts, while fine-tuning requires sufficient data and suitable techniques.

10 LLMS AS PERSONALIZED CONTENT CREATOR

LLMs expand recommender systems from selecting existing items toward generating personalized content. Their reasoning, feedback handling, and generative abilities offer new opportunities, but factuality and privacy risks require careful safeguards.

  • Personalized content creation: Personalized content creators generate customized material, unlike traditional recommenders that retrieve already-generated items.Text ad generation is one example, with titles and descriptions tailored using personalized information.
  • Personalized content creation: Data-driven content generation uses user feedback as rewards, while pre-trained language models support newer personalized-generation methods.Earlier template-based approaches reduced human effort but often failed to fully match user interests and preferences.
  • Opportunities from LLMs: LLMs may improve personalized content creation through stronger intent reasoning, RLHF-based alignment, and realistic generation supported by crossmodal knowledge.The paper identifies these as opportunities for extending prior tailored pre-training and reinforcement-learning approaches.
  • Opportunities from LLMs: Interactive feedback and multiple conversation rounds can capture explicit user preferences and convert them into generation instructions.This significantly alleviates the extremely sparse-feedback problem described for previous training paradigms.
  • Security and privacy: Personalized content creators face security risks from misleading outputs and privacy risks involving user profiles and long-term interaction histories.The passage notes that language models can memorize and leak individual training examples, motivating strict approval and careful data handling.

11 OPEN CHALLENGES

Deploying LLMs in personalization introduces infrastructure, modeling, interpretability, evaluation, factuality, privacy, discrimination, and ethics challenges. Addressing these concerns may require balancing responsible behavior with system performance.

  • Scalability and latency: Real-world personalization must address computational cost and response latency from training, fine-tuning, inference, and concurrent requests.Efficient fine-tuning and precomputed intermediate embeddings are cited as possible approaches.
  • Knowledge and context: Open-domain training knowledge does not fully cover personalized tasks requiring private, specialized, or user-specific information.Recommender systems often rely on datasets such as MovieLens and Amazon Books, while personalized tasks require additional knowledge.
  • Knowledge and context: Limited context windows make long behavior sequences and multi-round conversations difficult to include effectively in prompts.The passage gives 4096 tokens for ChatGPT as an example context-window constraint.
  • Knowledge and context: Collaboratively modeling long text with recommendation tasks requires more complex modeling and may require reforming pipelines for efficient latency.Textual information offers useful item and user insights but is harder to handle than categorical features.
  • Interpretability and explainability: LLM recommendation systems remain difficult to interpret because their complex, nonlinear architectures obscure how recommendations are generated.This complicates bias detection, fairness assurance, user trust, and the production of meaningful explanations.
  • Evaluation: Evaluation must extend beyond ranking metrics to include user satisfaction, engagement, experience, novelty, diversity, serendipity, retention, interpretability, and fairness.These measures address generated-content quality, user-centered outcomes, system health, transparency, and bias.
  • Helpfulness, honesty, and harmlessness: LLM personalization risks misleading guidance, privacy violations, discrimination, and ethical harms, making accuracy, factuality, privacy, and security essential.The paper emphasizes rigorous protection of personal data and strict permissions for sharing user information.
  • Helpfulness, honesty, and harmlessness: Safeguarding privacy, mitigating discrimination, and following ethical guidelines can support user trust, fairness, and responsible recommendations.The paper frames these objectives as necessary for leveraging LLMs in recommender systems responsibly.

12 CONCLUSION

The paper presents LLMs as expanding personalization from performance enhancement toward complex interactions with external tools and end-to-end personalized services. It also acknowledges open challenges in this integration.

  • Conclusion: LLM-based personalization progresses from using low-level model capabilities to integrating external tools for complex, end-to-end tasks.The paper links this progression to new possibilities for how personalized services are delivered.
  • Conclusion: The integration of LLMs into personalization systems brings open challenges alongside its potential to transform personalized service delivery.The conclusion presents both the expanded possibilities and unresolved issues as central outcomes of the perspective.
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