… news articles. To address this issue, this study proposes a time-sensitive heterogeneous
graph similarity neural … the importance of short-term and long-term interests in user modeling. …

… a personalized dynamic recommendation model … long-term interest and short-term interest,
capturing the dynamic changes of users’ interests to improve the accuracy of recommendation…

… , personalized session-based recommendation is still a … ie, only personal long-term information
and short-term information, and … several of the most advanced recommendation methods. …

… of modeling users or news, the news recommendation … 推荐方法(graph neural news
recommendation with unsupervised … Neural news recommendation with long-and short-term user …

… 新闻推荐方法(neural news recommendation with long-and short-term user representation,…
法(graph neural news recommendation with user existing and potential interest modeling,…

… a set of topic/history modeling techniques that broadly can be … network (RNN) [12][13], long
short-term memory (LSTM) [14][15… In this review, we explore the use of graph embeddings of …

… Base on the data collected it is possible to draw a short-term … The following form of VEC
Models is used in this study: … the actual distance between the graphs of the two variables. In this …

… network Recommender … graphs through Graph Convolutional neural Network (GCN); (2)
high-quality user and product representation vectors can be obtained, and realize the modeling …

… This paper proposes a Long Short-Term Memory (LSTM) neural … chart of scores, verification
losses and average early warning time of different models, and (b) scores of different models …