Implicit positive and negative feedback
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- Dominant language
- Python
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Description
Hello,
Thanks for the great package!
I am new to recommendation systems, and I wonder how could I model and specify negative implicit feedback using LightFM:
- In my problem, customers receive personalized products every number of months; they can keep products they like (purchase) and return the ones don't want (negative feedback)
- The rest will be just missing data (unknown products for customers)
For now, I am just removed from each customer recommendation list previously sent products that were not purchased. There may be better ways to include implicit negative information in the model, but I don't know if that is possible.
Thanks!
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Research direction
The issue names no file, test, or entry point. Start by examining how LightFM currently represents implicit positive feedback and whether returned products can be represented separately from unknown products; done would require a clearly scoped, documented approach or feature decision for negative feedback.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100