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.
Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization
Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）；Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯大学）
Rethinking Personalization in Large Language Models at the Token Level
Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）；State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室，北京大学智能科学与技术学院）；School of Mathematical Sciences, Peking University（数学科学学院，北京大学）
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Graduate School of AI, KAIST（韩国科学技术院人工智能研究生院）；Samsung Advanced Institute of Technology（三星先进技术研究院）；Department of Electrical and Computer Engineering, UBC（不列颠哥伦比亚大学电气与计算机工程系）
Syntax-Guided Diffusion Language Models with User-Integrated Personalization
Department of Statistics and Data Science, Fudan University（复旦大学统计与数据科学系）；Department of Statistics and Applied Probability, University of California, Santa Barbara（加州大学圣巴巴拉分校统计与应用概率系）
Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning
ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Virginia（弗吉尼亚大学）；University of Colorado Anschutz（科罗拉多大学安舒茨分校）；Johns Hopkins University（约翰霍普金斯大学）；University of Chicago Pritzker School of Medicine（芝加哥大学普ritzker法学院）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国家卫生研究院）；University of Washington（华盛顿大学）
A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；School of Business, Singapore University of Social Sciences（新加坡国立大学商学院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
RespAI Lab, KIIT Bhubaneswar（KIIT布巴内斯瓦尔 RespAI 实验室）；BITS Pilani（皮拉尼比尔拉理工学院）；University of South Florida（南佛罗里达大学）；Pennsylvania State University（宾夕法尼亚州立大学）
University of Colorado, Boulder（科罗拉多大学博尔德分校）；University of Minnesota（明尼苏达大学）；eBay, Inc.（eBay公司）；University of Klagenfurt（克拉根福大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Expedia Group（Expedia集团）；Accenture（埃森哲）
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unif…
Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product related documents, leaving the user collaborative behaviors untapped. In fact, the collaborative filtering signals between users intuitively offer a complementary information for the semantic matching. To close the gap between collaborative filtering and product search, we propose a Hierarchical Heterogeneous Graph Neural Network (HHGNN) approach in this paper. Specifically, we organize HHGNN with a hierarchical grap…
