Session-based recommendation (SBR) is making item recommendations based on anonymous click behavior.SBR based on graph neural networks has shown great power in recent years.It can enhance the representation of items in a session.The aggregation of items is then used to generate a session vector for the recommendation.
However, existing SBR models rarely consider the impact of dwell-time in session data when performing session item aggregation.The Nakiri Knives dwell-time contains the implicit behavior of anonymous users in the session sequence.In order to obtain a more accurate session embedding and take into account the impact Speaker of multiple perspectives, we propose a new model, graph hierarchical dwell-time attention network.This approach uses a modified graph neural network to learn session items by extracting loss information from graph modeling.
We also design a hierarchical dwell-time attention module that uses the effect of dwell-time to generate long-term preferences for sessions.Experimental results show that GHDAN outperforms the state-of-the-art session-based recommendation methods.