  In the case that user profiles are not available, the recommendation based on
anonymous session is particularly important, which aims to predict the items
that the user may click at the next moment based on the user's access sequence
over a while. In recent years, with the development of recurrent neural
network, attention mechanism, and graph neural network, the performance of
session-based recommendation has been greatly improved. However, the previous
methods did not comprehensively consider the context dependencies and
short-term interest first of the session. Therefore, we propose a context-aware
short-term interest first model (CASIF).The aim of this paper is improve the
accuracy of recommendations by combining context and short-term interest. In
CASIF, we dynamically construct a graph structure for session sequences and
capture rich context dependencies via graph neural network (GNN), latent
feature vectors are captured as inputs of the next step. Then we build the
short-term interest first module, which can to capture the user's general
interest from the session in the context of long-term memory, at the same time
get the user's current interest from the item of the last click. In the end,
the short-term and long-term interest are combined as the final interest and
multiplied by the candidate vector to obtain the recommendation probability.
Finally, a large number of experiments on two real-world datasets demonstrate
the effectiveness of our proposed method.
