融合深度神经网络和张量分解的地点推荐算法

为提高利用张量分解技术进行基于位置社交网络的地点推荐的推荐性能,提出一种利用张量分解技术且融合神经网络的地点推荐算法。融合多层感知机和长短期记忆网络基于张量分解技术建模用户的签到行为,将学习到的用户偏好表示馈送到推荐生成器和推荐判别器组成的对抗生成网络中,通过对抗训练学习最佳用户偏好表示用于推荐。基于真实数据集的实验验证了该算法的有效性和高效性。

Location recommendation algorithm combined with tensor factorization and deep neural network

To solve the problem of low performance of location recommendation in location-based social network using tensor factorization technology,a location recommendation algorithm combining neural network and tensor factorization was proposed.The multi-layer perceptron and long-and short-term memory network were combined to model the user’s check-in behavior based on tensor factorization technology.The learned user preference representation was fed into a generated countermeasure network composed of a recommendation generator and a recommendation discriminator,and the optimal user preference representation was learned through confrontation training for recommendation.Experimental results based on real data sets verify that the proposed recommendation framework is superior to the state-of-the-art location recommendation model.And the accuracy of the algorithm is improved.