GRIP: Generation and Reasoning for User Profile Completion

Abstract

User profiles, such as age and interest tags, form the backbone of modern recommender systems. However, in real-world scenarios, user profiles frequently encounter the problem of incomplete profile data, restricting the effectiveness of downstream recommendation tasks. Although large language models (LLMs) have shown remarkable potential in understanding user profiles, existing methods mainly focus on extracting explicit user information from external data and generating profile summaries. Such shallow reasoning patterns often fail to accurately infer missing user attributes under sparse data conditions. In this paper, we present GRIP (Generation and Reasoning from Incomplete Profiles), the first foundation model tailored for comprehensive profile inference under data sparsity for recommendation. GRIP introduces a unified three-stage training framework: (1) continual pre-training on large-scale structured user data for robust correlation modeling; (2) decomposed chain-of-thought reasoning with iterative self-distillation to facilitate in-depth profile inference; and (3) reinforcement learning with multi-dimensional rewards to jointly optimize factuality and reasoning coherence. Comprehensive experiments on realistic benchmarks along with online business deployments have demonstrated that GRIP significantly outperforms LLM-based methods in completing missing attributes and delivers substantial business gains in downstream recommendation tasks.