Conversational Recommendation Based on Graph Neural Network Model with Dual Attention Mechanism

For the session recommendation scenario, user behaviors in the same session are intrinsically related, and the context information of user session behavior is introduced into the session, and the behavior in the session is modeled. Introducing a dual attention mechanism, assigning different weights to user input behavior data, constructing a model, and conducting experiments on two public datasets. Compared with the benchmark model, this model has improved in various evaluation indices, proving its effectiveness.