智能推荐可能诱使用户陷入“信息茧房”“过滤气泡”和“回音室”等过度特化困境。考虑到心理特质及其产生的心理图式是用户外在偏好行为的内在表征，探索和验证心理图式在缓解用户过度特化倾向中的潜在作用，对完善以用户为中心的推荐方法及其评价体系具有理论和实践意义。文章基于用户心理图式理论，利用大规模用户偏好行为数据、自然语言处理和机器学习实验方法，探索和验证用户心理图式对其推荐多样性需求水平（即“特化”倾向）的影响。进而整合心理图式构建用户偏好模型，进行推荐算法实验加以验证。结果发现：心理图式能较好地解释和预测用户对推荐多样性的需求水平；心理图式能在保证推荐准确性的同时缓解过度特化问题；相对于人格特质，价值观在解释和应对用户过度特化问题上具有相对优势。

Intelligent recommender system may trap users in phenomena such as "information cocoons," "filter bubbles," and "echo chambers," leading to over-specialization. Individual psychological traits are internal representations of external behavior. Exploring and validating the potential role of online users' psychographics in mitigating their over-specialization tendencies，holds theoretical and practical significance for improving and evaluating of user-centered recommender system. Using natural language processing and machine learning experimental methods, based on psychographic segmentation theory, this study investigates the relationship between users' demand for recommendation diversity (i.e., "specialization" tendency) and their psychological traits through opinion mining and psychological computation methods. It then integrates psychographics into user preference models and validates them through large-scale preference behavior data and recommendation algorithm experiments. The study finds that: psychographics can effectively explain and predict users' demand for recommendation diversity. By integrating user preference models, psychographics can mitigate over-specialization while ensuring recommendation accuracy. Compared to personality traits, values have a relative advantage in explaining and mitigating users' over-specialization tendencies.