基于时空循环神经网络的下一个兴趣点推荐方法

下一个兴趣点推荐已经成为基于位置的社交网络(location-based social networks,LBSNs)中一个重要任务.现有的模型没有深入考虑相邻签到兴趣点之间的转移时空信息,无法对用户访问下一个兴趣点的长短时间偏好和远近距离偏好进行有效建模.本文通过对循环神经网络(recurrent neural ne...

A recurrent neural network model based on spatial and temporal information for the next point of interest recommendation

The next point-of-interest (POI) recommendation has become an important task in location-based social networks. The existing models lack in-depth research on the temporal and spatial information transition between adjacent check-in POIs and cannot effectively model the long/short time and distance preferences of the users accessing the next POI. In response, this paper proposes a new session-based spatial–temporal recurrent neural network (SST-RNN) model that is used to recommend the next POI. This model takes advantage of the spatial transition matrix and temporal transition matrix to respectively model the user’s spatial and temporal preferences, and comprehensively considers the sequence information and spatial–temporal information of consecutive check-in POIs as well as user preferences to do the next POI recommendation. Experimental results in two real open datasets show that the performance of the proposed SST-RNN model is significantly enhanced compared with the state-of-the-art models. On the Foursquare and CA datasets, the ACC@5 is increased by 36.38% and 13.81%, and the MAP is increased by 30.72% and 17.26%, respectively.