基于协同训练的分布式深度协同过滤模型

为解决数据分布式存储下实现较高精度和安全性的个性化推荐,提出了一种全新的分布式半监督推荐系统框架。尝试将半监督学习方法中的协同训练(Co-training)与基于深度学习的深度协同过滤模型结合为Co-NCF模型,并使用基于consensus算法的分布式梯度下降法来训练Co-NCF模型,以此构建了Co-NCF模型的分布式版本。该模型在MovieLens数据集上的测试中,表现显著强于现有的分布式NCF模型。

Research on Distributed Deep Collaborative Filtering Model Based on Co-Training

In order to realize the personalized recommendation with high accuracy and security, a new framework of distributed semi-supervised recommendation system was proposed. Co-NCF model was established through the combination of the co-training of semi supervised learning method with deep collaborative filtering model based on deep learning. Consensus-based distributed gradient decent algorithm was employed to train the Co-NCF model, so as to build the distributed version of Co-NCF model. In the test of MovieLens dataset, the performance of this model was significantly better than that of the existing distributed NCF model.