  Session based recommendation has become one of the research hotpots in the
field of recommendation systems due to its highly practical value.Previous deep
learning methods mostly focus on the sequential characteristics within the
current session,and neglect the context similarity and temporal similarity
between sessions which contain abundant collaborative information.In this
paper,we propose a novel neural networks framework,namely Neighborhood Enhanced
and Time Aware Recommendation Machine(NETA) for session based recommendation.
Firstly,we introduce an efficient neighborhood retrieve mechanism to find out
similar sessions which includes collaborative information.Then we design a
guided attention with time-aware mechanism to extract collaborative
representation from neighborhood sessions.Especially,temporal recency between
sessions is considered separately.Finally, we design a simple co-attention
mechanism to determine the importance of complementary collaborative
representation when predicting the next item.Extensive experiments conducted on
two real-world datasets demonstrate the effectiveness of our proposed model.
