Advantages of GraphAgent in e-commerce recommendation domains
GraphAgent's text-graph co-generation capability makes it an ideal tool for e-commerce recommendation system research. By simulating the complex interaction network of "user-item-rating", researchers can obtain a close-to-realistic test dataset, which is valuable for the development and validation of recommendation algorithms.
To implement this, the user simply executes thepython main.py --task movielens --config "small" --buildcommand to generate standard movie rating networks. The project also provides interfaces to standard datasets such as Movielens to facilitate controlled experiments. For Chinese e-commerce scenarios, the framework also supports Chinese text processing, users can modify the prompt template to adapt to different language environments.
Compared with traditional static datasets, the dynamic graphs generated by GraphAgent can more realistically reflect the evolution of user preferences. E-commerce platforms can use this feature to simulate the impact of promotional activities, analyze the segmentation characteristics of user groups, or test the effectiveness of new recommendation strategies, and ultimately improve conversion rates and user satisfaction.
This answer comes from the articleGAG: Generating a Social Relationship Graph Using a Large Model to Simulate Human BehaviorThe
