用推荐列表推断用户画像，提升多场景下个性化分析能力
Multi-Scenario User Profile Construction via Recommendation Lists
- 通过推荐列表构建代理模型，结合BERT提取物品嵌入
- 样本增强生成扩展推荐列表，提升特征推断精度
- 自适应加权分类模型实现动态特征推理，适合数据稀缺场景
推荐系统在商业分析等领域发挥核心作用，帮助用户与企业做出合理决策。为优化服务品质，现有技术聚焦于通过分析用户历史行为构建用户画像。本文针对四种不同信息条件下的分析场景，评估用户画像构建能力。提出一种通用的用户属性分析框架RAPI，通过挖掘易获取的推荐列表来推断用户个人特征。具体而言，建立代理推荐模型以模拟原模型，利用预训练BERT模型的内容嵌入获得物品嵌入；设计样本增强模块，结合模型输出与物品嵌入的相似性生成扩展推荐列表；最后采用自适应权重分类模型，动态分配权重以辅助用户特征推断。在四个数据集上的实验表明，RAPI在不同场景下分别达到0.764和0.6477的推断准确率。
原文摘要 · Abstract (English)
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.
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