Multi-Scenario User Profile Construction via Recommendation Lists

2026-03-16

AI Summary

This study addresses the challenge of inferring user demographic attributes across multiple scenarios using only recommendation lists, without access to explicit user profiles or interaction histories. To this end, the authors propose RAPI, a general-purpose framework that simulates the original recommender to generate augmented recommendation lists, integrates BERT-based content embeddings, and employs a dynamically weighted classifier to enhance inference performance. RAPI is the first approach to enable cross-scenario user attribute inference solely from recommendation lists, introducing both sample augmentation and adaptive weighting mechanisms to mitigate data sparsity. Extensive experiments on four real-world datasets demonstrate its effectiveness, achieving accuracy scores of 0.764 and 0.6477, respectively, and significantly outperforming baseline methods, thereby validating its robustness and generalization capability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Recommender systems (RS) play a core role in various domains, including business analytics, helping users and companies make appropriate decisions. To optimize service quality, related technologies focus on constructing user profiles by analyzing users' historical behavior information. This paper considers four analytical scenarios to evaluate user profiling capabilities under different information conditions. A generic user attribute analysis framework named RAPI is proposed, which infers users' personal characteristics by exploiting easily accessible recommendation lists. Specifically, a surrogate recommendation model is established to simulate the original model, leveraging content embedding from a pre-trained BERT model to obtain item embeddings. A sample augmentation module generates extended recommendation lists by considering similarity between model outputs and item embeddings. Finally, an adaptive weight classification model assigns dynamic weights to facilitate user characteristic inference. Experiments on four collections show that RAPI achieves inference accuracy of 0.764 and 0.6477, respectively.

Problem

• user profiling
• recommendation lists
• multi-scenario
• attribute inference
• recommender systems

Innovation

• user profiling
• recommendation lists
• surrogate model
• sample augmentation
• adaptive weighting