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

## TL;DR

This paper presents a method for creating automatic questionnaires to understand new Wikipedia users' interests, enabling personalized article recommendations without prior user interaction data, thus addressing the cold-start problem.

## Contribution

It introduces a novel approach for generating user interest questionnaires from Wikipedia content and editor history to improve recommendations for newcomers.

## Key findings

- Questionnaires are human-readable and cohesive.
- The method outperforms several baselines in offline tests.
- Online evaluations show positive engagement with new users.

## Abstract

Every day, thousands of users sign up as new Wikipedia contributors. Once joined, these users have to decide which articles to contribute to, which users to seek out and learn from or collaborate with, etc. Any such task is a hard and potentially frustrating one given the sheer size of Wikipedia. Supporting newcomers in their first steps by recommending articles they would enjoy editing or editors they would enjoy collaborating with is thus a promising route toward converting them into long-term contributors. Standard recommender systems, however, rely on users' histories of previous interactions with the platform. As such, these systems cannot make high-quality recommendations to newcomers without any previous interactions -- the so-called cold-start problem. The present paper addresses the cold-start problem on Wikipedia by developing a method for automatically building short questionnaires that, when completed by a newly registered Wikipedia user, can be used for a variety of purposes, including article recommendations that can help new editors get started. Our questionnaires are constructed based on the text of Wikipedia articles as well as the history of contributions by the already onboarded Wikipedia editors. We assess the quality of our questionnaire-based recommendations in an offline evaluation using historical data, as well as an online evaluation with hundreds of real Wikipedia newcomers, concluding that our method provides cohesive, human-readable questions that perform well against several baselines. By addressing the cold-start problem, this work can help with the sustainable growth and maintenance of Wikipedia's diverse editor community.