Recommendation method and device based on deep learning and Markov chain

The invention provides a recommendation method and device based on deep learning and a Markov chain, and relates to the technical field of machine learning. The method comprises the steps of determining a transition probability of a Markov chain corresponding to a target user according to historical behavior information of the target user watching a video; calculating a target embedding vector according to the transition probability; and obtaining a recommended target video according to the target embedding vector and the attribute information of the target user. According to the scheme of theinvention, the accuracy of user recommendation and the convergence rate during training are improved. 本发明提供一种基于深度学习与马尔科夫链的推荐方法及设备，涉及机器学习技术领域。该方法包括：根据目标用户观看视频的历史行为信息，确定所述目标用户对应马尔科夫链的转移概率；根据所述转移概率，计算目标嵌入向量；根据所述目标嵌入向量和所述目标用户的属性信息，得到推荐的目标视频。本发明的方案，提升了针对用户推荐的准确性和训练时的收敛速度。