Peer production platforms like Wikipedia commonly suffer from content gaps.
Prior research suggests recommender systems can help solve this problem, by
guiding editors towards underrepresented topics. However, it remains unclear
whether this approach would result in less relevant recommendations, leading to
reduced overall engagement with recommended items. To answer this question, we
first conducted offline analyses (Study 1) on SuggestBot, a task-routing
recommender system for Wikipedia, then did a three-month controlled experiment
(Study 2). Our results show that presenting users with articles from
underrepresented topics increased the proportion of work done on those articles
without significantly reducing overall recommendation uptake. We discuss the
implications of our results, including how ignoring the article discovery
process can artificially narrow recommendations. We draw parallels between this
phenomenon and the common issue of "filter bubbles" to show how any platform
that employs recommender systems is susceptible to it.Comment: To appear at the 18th International AAAI Conference on Web and Social
Media (ICWSM 2024