Collaborative Filtering
Collaborative filtering uses the wisdom of ‘similar’ crowd in order to find recommendations for user A based on interests of a similar user B.
This method has popularity bias. Hence:
- It needs to be re-trained every few minutes (or hours) so that the model stays up to date with what’s popular.
- It cannot be used for use-cases like recommendations in a pharmacy.
Why it matters: At scale, most products cannot be targeted. For example, a specific SUV might be targeted towards off-roading enthusiasts but an urban professional who had a recent hike might also buy it not because the product was built for him but because they can.
Collaborative filtering typically requires matrix factorization.
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