论文标题
属性感知的多元化用于顺序建议
Attribute-aware Diversification for Sequential Recommendations
论文作者
论文摘要
用户更喜欢多种建议，而不是同质的建议。但是，大多数先前关于顺序推荐人的工作都不考虑多样性，并努力提高准确性，从而提出同质建议。在本文中，我们通过提出属性多样化的顺序推荐器（ADSR）来考虑准确性和多样性。具体来说，ADSR在建模用户的顺序行为时，会使用可用的属性信息，以同时了解用户最有可能与之交互的项目及其对属性的偏爱。然后，ADSR根据对某些属性的预测偏好将推荐的项目多样化。两个基准数据集的实验表明，ADSR可以有效地提供不同的建议，同时保持准确性。
Users prefer diverse recommendations over homogeneous ones. However, most previous work on Sequential Recommenders does not consider diversity, and strives for maximum accuracy, resulting in homogeneous recommendations. In this paper, we consider both accuracy and diversity by presenting an Attribute-aware Diversifying Sequential Recommender (ADSR). Specifically, ADSR utilizes available attribute information when modeling a user's sequential behavior to simultaneously learn the user's most likely item to interact with, and their preference of attributes. Then, ADSR diversifies the recommended items based on the predicted preference for certain attributes. Experiments on two benchmark datasets demonstrate that ADSR can effectively provide diverse recommendations while maintaining accuracy.
