GapFinder



GapFinder is a set of tools designed to find and provide methods to fill content coverage gaps in Wikipedia. It is being actively developed in order to more flexibly test recommendation algorithms' impacts on content creation and gather further feedback from users about the service.

Try it
The experimental tool is hosted on Wikimedia Labs. Information collected when you visit this site, and through your use of the tool, is governed by this privacy statement (not the main Wikimedia Privacy Policy).

You can access the tool at https://recommend.wmflabs.org.

Feedback is welcome |at the talk page. Feature requests and bug reports can also be [ submitted through Phabricator].

Purpose
"Each day, thousands of volunteer editors are filling knowledge gaps by creating new Wikipedia articles, translating existing ones, and identifying poorly covered topics in any given language. However, discovering and deciding what to edit can be a daunting task, both for editors who are new to Wikipedia and for more-seasoned ones."

GapFinder is a set of tools to provide personalized recommendations of tasks to editors.

Translation
Recommend articles for creation that exist in one language but are missing in another.

Features
We know from the first experiment that personalized recommendations perform much better than random recommendations when it comes to user activation rates and article creation rate. Ideally, we want to have the personalized recommendations based on users' edit history available in the tool. However, we also want to reduce the barrier for entry by showing recommendations to users before they are required to login via centralAuth, and we also want to allow users with almost no edit history to be able to get recommendations about articles they may be interested in. We make this possible by providing an option for the user to enter an article that he/she would have liked to edit (or he/she has edited and liked it) in the source language of their choice, and get recommendations that are similar to his/her article of liking in the destination language based on a topical model that algorithm operates on.

How does it work?
The recommendation works in two ways. Once the source and destination languages are chosen, a random subset of articles that are available in the source language but are missing in the destination language and are predicted by the algorithm to be eligible to exist in the destination language are shown to the user. The user can receive personalized recommendations by providing an article name in the source language that the user would contribute to in the destination language and by clicking Recommend, the user receives similar articles missing in the destination language but available in source.

Related pages

 * Research paper
 * Research project
 * Blog post

Missing Sections
Recommend sections to add to existing articles.
 * Research project