Research On Session Recommendation Based On Graph Attention And Transfer Learning

In order to make up for the deficiencies of traditional recommendation algorithms.which are based on user identity and historical behaviors,session-based recommendation algorithms have received more and more attention in recent years.Unlike traditional recommendation methods,session-based recommendation captures users’ short-term preferences from their most recent sessions for more accurate and timely suggestions.However,the current session-based recommendation algorithm often encounters two challenges in its practical application:the one is that in the process of session representation learning,irrelevant items have an unavoidable impact on feature extraction,while the internal association between different sessions needs to be further explored;Another major challenge is that the training data on a scarce commodity recommendation or newly established commerce platform cannot meet the training needs of the existing recommendation models and cannot be sufficient to train robust and stable recommendation algorithms.Therefore,the following works have been done to solve the above problems:(1)In order to reduce the influence of noisy items on session representation learning and mine the relationship between different sessions,a session-based recommendation algorithm based on the graph dual attention model is proposed in this paper.The sparse self-attention learning module of the graph dual attention model can give a zero value attention weight to the user’s wrong clicks,so as to reduce the negative impact of noise items on session representation learning.Then,the graph attention learning module combines the dual attention session representation and graph convolution neural network to further mine the relationship between different sessions,and obtain a more robust graph dual attention session representation.In this way,we can build a more reliable session representation for the session sequence,thus improving the recommendation performance.(2)Aiming at the problem of few-shot session recommendation with insufficient training samples in practical application scenarios,this paper proposes a few-shot session-based recommendation algorithm based on the graph attentive transfer learning model.The intra-session attention representation learning module in graph attentive transfer learning model focuses on mining the internal relationship between different items in each session and studying the importance of each item.The cross-domain inter-session representation learning module constructs the graph relationship for session samples between different domains.It carries out session representation learning in combination with the adversarial transfer learning mechanism.Thus,the learned session representation integrates the session data characteristics of the source domain and effectively solves the problem of few-shot session-based recommendation in the target domain.(3)To verify the validity of the two methods proposed in this thesis,a number of experiments have been carried out on two public datasets,Retailrocket and Diginetica.The experimental results show that the proposed graph dual attention model achieves better results in HR@20 and MRR@20 metrics than advanced benchmark models.The graph attentive transfer learning model proposed in this thesis performs well in solving few-shot learning session-based recommendation problem.