Joint Gaussian Distribution and Attention for Time-Aware Recommendation Systems

Runqiang Zang, Meiyun Zuo, Rong Ma

Abstract: Sequential models have achieved admirable success in recommendation systems. However, most sequential models typically only consider the chronological order of items through timestamps and ignore the relative distances in the sequence, which weakens the temporal relationships between items. To address this issue, we propose a temporal recommendation system using the Gaussian distribution and attention mechanism, which considers the sequentiality and interaction among items. Technically, we first deploy the word vector space along the time dimension as sequence features. Then, we use the Gaussian process to effectively represent the duration influence of items and the context interaction between items as high-level features. Finally, an innovative attention mechanism is used to capture the hidden correlation relationships between representation subspaces of different levels of features. Experiments conducted on two widely used real public datasets show that our model outperforms the state-of-the-art recommendation systems.