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Show HN: Personalized News Recommendation System Using Graph Neural Networks(personalized-news.example.com)

98 points by gnn_enthusiast 7 months ago flag hide 12 comments

techguru 7 months ago next
Fascinating approach! I think using GNN for personalized news rec system can really yield amazing results. Would love to hear more about how you handle cold start problem?
- techguru 7 months ago next
  @TechGuru the cold start problem is solved by utilizing collaborative filtering methods along with graph neural networks. It ensures recommendations until enough personalization data is collected.
datamind 7 months ago prev next
Great project! I'm wondering how the model deals with the negative impacts of popularity bias in news recommendations?
- recomengineer 7 months ago next
  @DataMind A rank-based re-balancing algorithm is used to mitigate popularity bias within the news recommendations. Interesting paper on the topic: [news.ccs.neu.edu/home/vazirg/…](http://news.ccs.neu.edu/home/vazirg/papers/KDD13-PopBias.pdf)
deepcodefan 7 months ago prev next
This is a really promising application of GNNs! Have you considered any potential methods for providing explainability on the news recommendations?
- techguru 7 months ago next
  @DeepCodeFan We utilized attention mechanisms within the GNN, allowing for better explainability in recommendations. Users can observe which nodes have a higher impact on recommendation outputs.
neuralnacho 7 months ago prev next
News rec systems often deal with temporal dynamicity. How does your model handle shifts in users' interests and News' popularity over time?
- recomengineer 7 months ago next
  @NeuralNacho Our model is trained to incorporate time decay into the graph neural networks, allowing it to consider recent interests and news popularity more strongly. An active learning strategy is also used, as discussed in our paper.
statsguru 7 months ago prev next
Impressive! I'm interested in learning about the evaluation metrics used to gauge the success of the model. Performance and fairness metrics, specifically?
- techguru 7 months ago next
  @StatsGuru Metrics include precision, recall, and F1, while fairness is measured by demographic parity and equal opportunity. Further details and experiments are discussed in the paper.
graphxpert 7 months ago prev next
Awesome project! Wondering what kind of graph neural network architecture you're utilizing and how the approach differs from traditional GNN applications?
- recomengineer 7 months ago next
  @GraphXpert The model utilizes a graph convolutional network (GCN) architecture within the encoder and a long short-term memory (LSTM) network in the decoder. Compared to traditional GNN, we are working with dynamic...`markdown(this.comment.slice(200))`

techguru 7 months ago next
Fascinating approach! I think using GNN for personalized news rec system can really yield amazing results. Would love to hear more about how you handle cold start problem?
- techguru 7 months ago next
  @TechGuru the cold start problem is solved by utilizing collaborative filtering methods along with graph neural networks. It ensures recommendations until enough personalization data is collected.
datamind 7 months ago prev next
Great project! I'm wondering how the model deals with the negative impacts of popularity bias in news recommendations?
- recomengineer 7 months ago next
  @DataMind A rank-based re-balancing algorithm is used to mitigate popularity bias within the news recommendations. Interesting paper on the topic: [news.ccs.neu.edu/home/vazirg/…](http://news.ccs.neu.edu/home/vazirg/papers/KDD13-PopBias.pdf)
deepcodefan 7 months ago prev next
This is a really promising application of GNNs! Have you considered any potential methods for providing explainability on the news recommendations?
- techguru 7 months ago next
  @DeepCodeFan We utilized attention mechanisms within the GNN, allowing for better explainability in recommendations. Users can observe which nodes have a higher impact on recommendation outputs.
neuralnacho 7 months ago prev next
News rec systems often deal with temporal dynamicity. How does your model handle shifts in users' interests and News' popularity over time?
- recomengineer 7 months ago next
  @NeuralNacho Our model is trained to incorporate time decay into the graph neural networks, allowing it to consider recent interests and news popularity more strongly. An active learning strategy is also used, as discussed in our paper.
statsguru 7 months ago prev next
Impressive! I'm interested in learning about the evaluation metrics used to gauge the success of the model. Performance and fairness metrics, specifically?
- techguru 7 months ago next
  @StatsGuru Metrics include precision, recall, and F1, while fairness is measured by demographic parity and equal opportunity. Further details and experiments are discussed in the paper.
graphxpert 7 months ago prev next
Awesome project! Wondering what kind of graph neural network architecture you're utilizing and how the approach differs from traditional GNN applications?
- recomengineer 7 months ago next
  @GraphXpert The model utilizes a graph convolutional network (GCN) architecture within the encoder and a long short-term memory (LSTM) network in the decoder. Compared to traditional GNN, we are working with dynamic...`markdown(this.comment.slice(200))`