论文标题
个性化文本分类的增量用户嵌入建模
Incremental user embedding modeling for personalized text classification
论文作者
论文摘要
个人用户概况和交互历史在在聊天机器人，社交媒体，零售和教育等现实世界应用中提供定制体验方面起着重要作用。通过使用用户个性化信息，由于不断增长的历史数据，自适应用户表示学习变得越来越具有挑战性。在这项工作中，我们提出了一种增量用户嵌入建模方法，其中用户最近的交互历史的嵌入方式通过变压器编码动态地集成到了累积的历史向量中。这种建模范式使我们能够以连续的方式创建广义的用户表示形式，并减轻数据管理的挑战。我们通过根据REDDIT数据集将其应用于个性化的多类分类任务来证明这种方法的有效性，并通过适当的评论历史记录编码和任务建模实现了两个实验设置的预测准确性的相对提高的9％和30％。
Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning by utilizing user personalized information has become increasingly challenging due to ever-growing history data. In this work, we propose an incremental user embedding modeling approach, in which embeddings of user's recent interaction histories are dynamically integrated into the accumulated history vectors via a transformer encoder. This modeling paradigm allows us to create generalized user representations in a consecutive manner and also alleviate the challenges of data management. We demonstrate the effectiveness of this approach by applying it to a personalized multi-class classification task based on the Reddit dataset, and achieve 9% and 30% relative improvement on prediction accuracy over a baseline system for two experiment settings through appropriate comment history encoding and task modeling.
