Finding what to do as a new user is one of the aspects that intersect with different issues surfaced by this research. Several ideas have emerged around the area of task recommendation/suggestions. So I thought it would be relevant to share our experience providing suggestions of articles to translate in Content Translation.
We wanted to suggest articles for editors to translate, and we wanted those suggestions to be relevant. The Language team worked with the Research team to integrate their recommendation system, but before the integration was ready the Language team used a very simplistic approach as a placeholder (showing featured articles that were missing in your language).
At some point we changed the basic suggestions algorithm to the more advanced one from Research. Content Translation was sending the recent translations from the user and getting relevant suggestions related to those. During user research around that time it was noticeable that the more advanced recommendations were working much better according to the users perception. But it also resulted in a big spike in the number of suggestions that users picked to create an article, going from under 50 per week to more than one thousand (the graph of that initial growth is shown at the side).
Since then, the number of suggestions selected by users to start a new translation has grow to more than ten thousands per week. We still don't know which percentage of those end up becoming successfully published articles, but our current data indicates that we at least are able to provide suggestions that are relevant enough for users to be interested in contributing on topics they may not contribute by themselves otherwise.
I just wanted to share the story to highlight that we have a powerful recommendation system that has proven useful in a specific context such as translation. Knowing that we are able to provide good recommendations may help to reduce the uncertainty when evaluating the risks of projects in this area.