Neighborhood-Enhanced and Time-Aware Model for Session-based Recommendation
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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 ignore collaborative information.SessionKNN is a strong baseline for session based recommendation since it utilizes the collaborative information from neighborhood sessions.However,SessionKNN neglects the sequential characteristics within the current session.To this end,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.
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