论文标题
关于属性驱动的协作过滤中用户建模的变异推断
On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering
论文作者
论文摘要
推荐系统已成为在线电子商务平台不可或缺的一部分，推动客户参与和收入。最受欢迎的推荐系统试图从用户过去的参与数据中学习，以了解用户的行为特征，并使用它来预测未来的行为。在这项工作中，我们提出了一种使用因果推理来通过时间上下文学习用户属性亲和力的方法。我们将此目标提出为概率的机器学习问题，并应用基于变异推理的方法来估计模型参数。我们在两个现实世界数据集上的下一个属性预测任务上演示了所提出的方法的性能，并表明它的表现优于标准基线方法。
Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from users' past engagement data to understand behavioral traits of users and use that to predict future behavior. In this work, we present an approach to use causal inference to learn user-attribute affinities through temporal contexts. We formulate this objective as a Probabilistic Machine Learning problem and apply a variational inference based method to estimate the model parameters. We demonstrate the performance of the proposed method on the next attribute prediction task on two real world datasets and show that it outperforms standard baseline methods.
