Journal
PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22)
Volume -, Issue -, Pages 2282-2286Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3477495.3531846
Keywords
Click-Through Rate; Attention; Spatial-temporal Embedding; Sequential Prediction
Categories
Ask authors/readers for more resources
This paper proposes a transformer-based spatial-temporal recommendation framework (STEM), which utilizes attention mechanisms and a transformer-based model to incorporate user behavior, item behavior, and spatial-temporal information for improving recommendation performance.
Sequential prediction is one of the key components in recommendation. In online e-commerce recommendation system, user behavior consists of the sequential visiting logs and item behavior contains the interacted user list in order. Most of the existing state-of-the-art sequential prediction methods only consider the user behavior while ignoring the item behavior. In addition, we find that user behavior varies greatly at different time, and most existing models fail to characterize the rich temporal information. To address the above problems, we propose a transformer-based spatial-temporal recommendation framework (STEM). In the STEM framework, we first utilize attention mechanisms to model user behavior and item behavior, and then exploit spatial and temporal information through a transformer-based model. The STEM framework, as a plug-in, is able to be incorporated into many neural network-based sequential recommendation methods to improve performance. We conduct extensive experiments on three real-world Amazon datasets. The results demonstrate the effectiveness of our proposed framework.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available