Recommendation Engine for Robust Personalization in Finetuning Campaigns
Recommender systems are useful for curating items to users.
Recommender Systems come mostly in two flavors: Content-Based and Collaborative. There are hybrid models which use both.
Content-based works on data generated by a user. This data can be explicit (e.g. Likes) or implicit (e.g. click-throughs). The data generated forms a user profile containing interaction metadata.
This is a social graph approach. It's as simple as 'Users who liked X also like Y'. Users are grouped by similarity in taste. Items can be grouped as well.
This approach is often used to balance trade-offs between Content and Collaborative models. It is typically used to address the 'Cold Start' problem.
The cold start problem is essentially the idea that models with sparse data don't perform well. If they don't perform well, it's hard to retain user traction to feed into the recommender system.
A system ideally has a robust, dense and large corpus. This allows subsetting into candidates.
Candidates are scored and ranked to provide their recommendation to the user. The user then gives input to the system to develop precision for additional queries.
Items given explicit negative input should be removed. Items that are newer are more likely to be recommended. This helps the items become diverse, fresh and fair.
Polarization may be measured by the extent in which user's ratings disagree.