How do you make a base recommendation system for an app without a large amount of data, assuming the data would be provided by the users?
Without any context, here are some approaches to consider:
- recommend your top "sellers"
- ask domain experts to come up with logical pairings of items
- randomize the recommendations
- is there an alternative system/model to borrow ideas from - from offline to online, an app in an adjacent field, ...?
- use information about content similarity (as described in https://www.bibblio.org/blog/three-ways-build-effective-recommender-system-without-audience-data)
I would probably use several different approaches like this in an A/B/x experiment mode, than refine it based on the actual choices the app users start making
Martijn recommends the following next steps:
Another idea is to search for a dataset in a search engine, such as Google Dataset search engine...
Find something similar that what you need, modify it as necessary, then presto! you have yourself some cool data to play with.