Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms
arxiv(2023)
摘要
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 on peer production platforms.
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