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Every day, thousands of users sign up as new Wikipedia contributors. Oncejoined, these users have to decide which articles to contribute to, which usersto seek out and learn from or collaborate with, etc. Any such task is a hardand potentially frustrating one given the sheer size of Wikipedia. Supportingnewcomers in their first steps by recommending articles they would enjoyediting or editors they would enjoy collaborating with is thus a promisingroute toward converting them into long-term contributors. Standard recommendersystems, however, rely on users' histories of previous interactions with theplatform. As such, these systems cannot make high-quality recommendations tonewcomers without any previous interactions -- the so-called cold-startproblem. The present paper addresses the cold-start problem on Wikipedia bydeveloping a method for automatically building short questionnaires that, whencompleted by a newly registered Wikipedia user, can be used for a variety ofpurposes, including article recommendations that can help new editors getstarted. Our questionnaires are constructed based on the text of Wikipediaarticles as well as the history of contributions by the already onboardedWikipedia editors. We assess the quality of our questionnaire-basedrecommendations in an offline evaluation using historical data, as well as anonline evaluation with hundreds of real Wikipedia newcomers, concluding thatour method provides cohesive, human-readable questions that perform wellagainst several baselines. By addressing the cold-start problem, this work canhelp with the sustainable growth and maintenance of Wikipedia's diverse editorcommunity.

Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start