Author:
Ma Manfu,Yang Dongliang,Li Yong
Abstract
Traditional session recommendation mainly uses the time sequence of users clicking items to construct a user session graph, which often ignores the similarity and differences between user groups. To improve the effect of recommendation, an E-SGNN (E-SGNN, Edge-Session Graph Neural Network) method combining edge information clustering and session recommendation is proposed. Firstly, similar users are clustered by edge information and divided into different session user groups. After extracting the data features of the user site relationship graph in the session, it is reset and updated through the gated graph neural network (GGNN); Secondly, a self-attention mechanism is introduced to adjust the proportion of users’ current preference and historical preference; Finally, the ranking score is obtained through linear transformation and softmax classifier. The higher the score, the more obvious the user’s preference for the item. Experiments show that compared with session-based graph neural network and cross-session information recommendation, the E-SGNN algorithm proposed in this paper has a significant improvement in recall rate and average reciprocal ranking. When the three edge parameters are combined, the recall rate reaches 98.97% and the average reciprocal ranking reaches 45.77%.
Subject
General Physics and Astronomy
Reference28 articles.
1. Graph neural networks in recommender systems: A survey;Wu;ACM Computing Survers,2021
2. A review of text-based recommendation systems;Kanwal;IEEE Access,2021
3. Review on recommendation system based on deep learning;Huang;Chinese Journal of Computers,2018
4. Recommendation systems: Algorithms, challenges, netrics, and business opportunities;Fayyaz;Applied Sciences,2020
5. Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction;Pi;Proc. of the 29 th ACM Inter. Conf. on Information & Knowledge Management,2020
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Medical Oral Image Super-Resolution Reconstruction Algorithm Based on Stable Diffusion Model;2023 International Conference on Artificial Intelligence and Automation Control (AIAC);2023-11-17