78 points by data_alchemist 2 years ago flag hide 15 comments
recommender 2 years ago next
Excited to see this post on exploring Graph Neural Networks (GNNs) for recommendation systems! I've been working on similar projects and it's great to see the community's interest in this topic.
gnnenthusiast 2 years ago next
Thanks for sharing! I'm particularly interested in learning how GNNs can improve collaborative filtering. Has anyone here applied GNNs to this problem with promising results?
collabfilter 2 years ago next
Yes, actually! My team and I implemented a GNN-based collaborative filtering approach that consistently outperformed traditional matrix factorization techniques. The key is representing user preferences as graph structures instead of dense matrices.
knowledgenet 2 years ago prev next
Graph Neural Networks can also improve knowledge graph-based recommendation systems. We've been experimenting with fusion of various knowledge graph embedding techniques and graph convolutional networks to refine node representations, resulting in more accurate recommendations.
algoexpert 2 years ago next
Could you elaborate on the specific embedding techniques that you've used? I've been looking for alternatives to TransE and its derivatives.
knowledgenet 2 years ago next
Sure! RotatE, DistMult, and ComplEx are potential alternatives that yield stronger results for specific recommendation contexts. We've been using a combination of these methods coupled with graph convolutional networks to build more robust representations.
machinelearn 2 years ago prev next
On the topic of GNNs and recommendation systems, it's interesting to consider how these techniques handle temporal dynamics. How do you address this challenge, if at all?
timeaware 2 years ago next
Excellent point! Temporal dynamics in recommendation systems can indeed be tricky to handle with GNNs. We address this issue by continuously updating node representations using a time-sensitive embedding strategy. It involves incorporating the recency of interactions in the graph structure and re-computing embeddings periodically.
gnndeepdive 2 years ago prev next
I've seen some interesting work regarding attention mechanisms in GNN-based recommenders to capture user preferences over time. This could be another approach to handle temporal dynamics.
graphxperts 2 years ago prev next
What libraries or frameworks do you recommend for building GNN-based recommendation systems? I'm currently working with PyTorch and would appreciate some expert opinions.
pytorchgal 2 years ago next
If you're looking for a PyTorch-based solution for GNNs, I'd recommend checking out Pytorch Geometric (PyG). It's an excellent library that offers diverse graph neural network modules and implementations, making it ideal for Recommender Systems.
tensorflowtek 2 years ago prev next
Meanwhile, if you prefer TensorFlow, TensorFlow R… [truncated due to character limit]
practicalgnn 2 years ago next
TF-Recommenders is a sufficient choice for TensorFlow users, and it includes specific functionality to deal with GNN-based recommendation, such as graph convolutional layers.
datascientist 2 years ago prev next
I'm curious if there are any benchmark tests or datasets available for GNN-based recommendation systems. For comparison purposes, how would you evaluate the performance of these complex models?
bigdatafan 2 years ago next
You can utilize Amazon's 'Graph Based Recommender System' and Netflix's 'MovieLens' datasets, among others, to evaluate the performance of your recommendations. Since GNN methods have additional computations, a fair evaluation approach should include the inference time and the trade-offs made in complexity in addition to predictive accuracy metrics.