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Show HN: Personalized News Aggregator with Machine Learning(github.io)

98 points by ml_news_app 6 months ago flag hide 15 comments

mlopsfan 6 months ago next
Great work! Using ML for news aggregation is very innovative. I'm curious about the algorithms you used for personalization. Could you share more details on that?
- newsmlguy 6 months ago next
  Thanks! We used a combination of collaborative filtering and content analysis through NLP for personalization. We have a blog post that goes into the details if you'd like to check it out: [URL]
- techgeek2023 6 months ago prev next
  I've built simple news aggregators before, but never thought of integrating ML techniques for personalization. This is really inspiring! What libraries and resources would you recommend to get started?
  newsmlguy 6 months ago next
  Great question! We used Python, TensorFlow, and scikit-learn as our main ML libraries. Additionally, spaCy was helpful for NLP tasks. You can find many tutorials and resources for getting started with these libraries. I personally recommend the scikit-learn documentation and the TensorFlow tutorials.
datasciencenewb 6 months ago prev next
This is really cool! I've been trying to get into ML but haven't quite figured out its use cases. This definitely helps me understand how ML can be useful in real-life scenarios. Thanks for sharing!
mlbeginner 6 months ago prev next
I'm still learning about ML, and I'm curious about how you trained the model. Do you have any advice on building a dataset for this kind of application?
- newsmlguy 6 months ago next
  For training the model, we collected user browsing and click data, and used web scrapers to gather articles' metadata and content. When gathering data, ensure you're abiding by applicable copyright laws and privacy regulations. Always anonymize data and use privacy-preserving techniques when training models with user data.
bautista 6 months ago prev next
This is an interesting project! It would be nice to know more about how it scales. How do you handle updating your model and the underlying data to keep the recommendations fresh?
- newsmlguy 6 months ago next
  We use incremental training to keep the model up-to-date and retrain it periodically with new user behavior data. We keep the latest few days of article metadata in memory and use a job queue to continuously process new articles and update the model.
deeplearninglover 6 months ago prev next
Awesome work! I would be interested in learning about any feedback or lessons learned from deploying this model. What were the main technical and organization challenges to get this working in a production setup?
- newsmlguy 6 months ago next
  Managing the data infrastructure and ensuring the model can handle real-time user queries were the most significant challenges. On the technical side, we needed to optimize the model to reduce inference time. For organization, we adopted DevOps methods and regularly reviewed our system's performance to identify and resolve bottlenecks.
aistudent 6 months ago prev next
This is a fantastic project! Did you consider using reinforcement learning (RL) to improve the personalization? I imagine a feedback loop could greatly benefit user satisfaction.
- newsmlguy 6 months ago next
  We did consider RL, but ultimately decided on using supervised learning because user satisfaction is not the only goal. Balancing the objective of serving new articles to users while keeping them satisfied was important for the platform's growth. RL might not have provided a good trade-off in this scenario.
hackerone 6 months ago prev next
@NewsMLGuy I noticed that there is no mention of security considerations in your post. How do you ensure user data privacy and prevent data leakage in such systems? #keepHNSecure
- newsmlguy 6 months ago next
  You're right, thank you for pointing that out, @HackerOne. To ensure data privacy, we anonymize all user data before training and use differential privacy techniques to prevent information leaks. We also incorporate access controls and strict encryption policies in our infrastructure. #keepHNSecure

mlopsfan 6 months ago next
Great work! Using ML for news aggregation is very innovative. I'm curious about the algorithms you used for personalization. Could you share more details on that?
- newsmlguy 6 months ago next
  Thanks! We used a combination of collaborative filtering and content analysis through NLP for personalization. We have a blog post that goes into the details if you'd like to check it out: [URL]
- techgeek2023 6 months ago prev next
  I've built simple news aggregators before, but never thought of integrating ML techniques for personalization. This is really inspiring! What libraries and resources would you recommend to get started?
  newsmlguy 6 months ago next
  Great question! We used Python, TensorFlow, and scikit-learn as our main ML libraries. Additionally, spaCy was helpful for NLP tasks. You can find many tutorials and resources for getting started with these libraries. I personally recommend the scikit-learn documentation and the TensorFlow tutorials.
datasciencenewb 6 months ago prev next
This is really cool! I've been trying to get into ML but haven't quite figured out its use cases. This definitely helps me understand how ML can be useful in real-life scenarios. Thanks for sharing!
mlbeginner 6 months ago prev next
I'm still learning about ML, and I'm curious about how you trained the model. Do you have any advice on building a dataset for this kind of application?
- newsmlguy 6 months ago next
  For training the model, we collected user browsing and click data, and used web scrapers to gather articles' metadata and content. When gathering data, ensure you're abiding by applicable copyright laws and privacy regulations. Always anonymize data and use privacy-preserving techniques when training models with user data.
bautista 6 months ago prev next
This is an interesting project! It would be nice to know more about how it scales. How do you handle updating your model and the underlying data to keep the recommendations fresh?
- newsmlguy 6 months ago next
  We use incremental training to keep the model up-to-date and retrain it periodically with new user behavior data. We keep the latest few days of article metadata in memory and use a job queue to continuously process new articles and update the model.
deeplearninglover 6 months ago prev next
Awesome work! I would be interested in learning about any feedback or lessons learned from deploying this model. What were the main technical and organization challenges to get this working in a production setup?
- newsmlguy 6 months ago next
  Managing the data infrastructure and ensuring the model can handle real-time user queries were the most significant challenges. On the technical side, we needed to optimize the model to reduce inference time. For organization, we adopted DevOps methods and regularly reviewed our system's performance to identify and resolve bottlenecks.
aistudent 6 months ago prev next
This is a fantastic project! Did you consider using reinforcement learning (RL) to improve the personalization? I imagine a feedback loop could greatly benefit user satisfaction.
- newsmlguy 6 months ago next
  We did consider RL, but ultimately decided on using supervised learning because user satisfaction is not the only goal. Balancing the objective of serving new articles to users while keeping them satisfied was important for the platform's growth. RL might not have provided a good trade-off in this scenario.
hackerone 6 months ago prev next
@NewsMLGuy I noticed that there is no mention of security considerations in your post. How do you ensure user data privacy and prevent data leakage in such systems? #keepHNSecure
- newsmlguy 6 months ago next
  You're right, thank you for pointing that out, @HackerOne. To ensure data privacy, we anonymize all user data before training and use differential privacy techniques to prevent information leaks. We also incorporate access controls and strict encryption policies in our infrastructure. #keepHNSecure