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I have been building a recommendation model to recommend certain questions in an interaction platform to users to help each other. I have calculated an affinity score between categories to find which top categories are to be recommended. But each category has questions by users in itself. The amount of the questions increases with every new post a user posts in a certain category. Now how can I choose which of these questions to recommend when I have chosen the category through my affinity score ? Do I make it random ? Do I display the questions which come first in the data base ? Or is there any better alternative ?

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Welcome to the site, I'll propose few alternatives below in increasing order of complexities.

  1. Simple sorting criteria: Think what the user wants to see and create a score for sorting questions. Ordering of question based on some criteria like number of answers or number of views it has already received. Show top N based on score.
  2. Derived sorting criteria: Use a combination of factors like number of answers, freshness of question, popularity of question based on number of views to create a derived score. Show top N based on score.
  3. Adding Discovery to (1-2): The options above will penalize new questions and run into typical explore Vs. exploit trade-off in any recommendation system. You can alleviate that by adding a random bump to score of new questions so that they get a chance to participate.
  4. Jointly learning categories and questions: You can setup your recommendation algorithm to actually work at question ID level and use categories as side information. Check this worked out example of LightFM library in Python. Coincidentally, it uses stackoverflow questions and categories as examples.

The first 3 options are easy to implement but biased. Fourth one will need some data transformations and I'm not sure if your objective is to model directly at question ID level.

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