Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing personalization approaches rely on implicit representations within model parameters, making it difficult to interpret user-specific preferences or effectively handle long-context dependencies. To address these challenges, we propose PrefReward, a novel preference-aware generative framework that explicitly models user styles through a structured preference matrix and integrates it into the decoding process as a reward signal. PrefReward consists of two stages: (1) extracting a user-specific preference matrix that summarizes individual stylistic tendencies, and (2) using the matrix to guide generation via a KL-divergence-based reward function. Experiments on the LongLaMP dataset show that PrefReward outperforms non-personalized and retrieval-based baselines in both generation quality and personalization interpretability.
Yue Wu, Chengbing Wang, Yimeng Bai et al.· 0 citations
Large language models (LLMs) and agentic AI systems are rapidly moving into user-facing applications, yet most remain fundamentally generic, optimized for population-level objectives under the assumption that one model can serve all users. This assumption is increasingly misaligned with real-world deployment, where AI systems interact continuously with individuals whose preferences, knowledge, goals, and values evolve over time. PILA'26 is motivated by the need to move beyond static general models toward personal intelligence ---AI systems that explicitly model users and dynamically adapt their reasoning, behavior, and decisions through memory, interaction, and lifelong learning. The workshop brings together researchers and practitioners from data mining, LLMs, NLP, IR, human-centered AI, and AI safety to position personalization as a central research direction for next-generation AI systems at KDD. Topics include user memory and personalized alignment, self-evolving and lifelong learning, datasets and evaluation, real-world applications, and trustworthiness in user-adaptive AI. Workshop website: https://pila26-workshop.github.io.
Xiaoyan Zhao, Yang Zhang, Moxin Li et al.· Proceedings of the 32nd ACM...· 0 citations
