The rapid growth of Internet services and mobile devices provides an
excellent opportunity to satisfy the strong demand for the personalized item or
product recommendation. However, with the tremendous increase of users and
items, personalized recommender systems still face several cha
本文提出了一种基于自编码器的模型,即 SAE-NAD,用于学习非线性用户 - 地点关系,通过采用多维关注机制自适应地区分用户偏好程度,并通过 POI 嵌入与径向基函数内积相结合的方法实现在检查的 POI 的相似和附近邻居上使用户到达更高的可达性,并在三个真实世界数据集上进行了广泛的实验,证明了本模型的有效性。