My employer owns different regional newspapers, local tv stations and special-interest magazines.
In a way to increase page views and usage time, we would like to make the recommendations to our users ("you may also like...") as relevant as possible.
For that we're looking for a recommender system that works throughout our pages. Our requirements:
- Collect data as implicit as possible: We don't want users to star-rate content, but would rather collect what and/or how long they read/watch our content.
- The context of our content is always an article
- A minimal article has a title, a lead and a body (which can be text, a video or an image gallery)
- Most of our users are anonymous resp. can't be matched to a UUID
- We love Ruby, so a ruby-based recommender system would be appreciated (but is not a prerequisite)
- Should run on Linux.
What kind of collaborative filtering, content-based filtering or hybrid recommender system would you use in our case and why?