Multi-modal adversarial learning type video recommendation method and system

The invention relates to the field of computers and artificial intelligence, and provides a multi-modal adversarial learning type video recommendation method and system. According to the method, imageinformation of recommended projects is introduced, key technologies such as hierarchical kernel descriptor features, cross-modal semantics and adversarial learning are fused into a Bayesian personalized sorting model, an MVABRP model is constructed, and a group of most relevant projects are optimized based on the MVABPR model and recommended to users. According to the method or system, the recommendation task can be completed based on the heterogeneous data (the user scoring matrix and the image), the problem of data sparsity in recommendation is relieved to a certain extent, and the individuation degree of recommendation is improved. 本发明涉及计算机、人工智能领域，提供了一种多模态对抗学习型视频推荐方法和系统。本发明的方法通过引入被推荐项目的图像信息，将层次核描述子特征、"跨模态语义"、对抗学习等关键技术融入贝叶斯个性化排序模型中，构造出MVABRP模型，基于MVABPR模型优选一组最相关的项目推荐给用户。根据本发明的方法或系统，可基于异构数据（用户评分矩阵、图像）完成推荐任务，在一定程度