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.

翻译：对等生产平台（如维基百科）普遍存在内容缺口问题。先前研究表明，推荐系统可通过引导编辑者关注代表性不足的主题来帮助解决此问题。然而，尚不明确这种方法是否会导致推荐相关性降低，从而减少用户对推荐内容的整体参与度。为解答此问题，我们首先对维基百科的任务路由推荐系统SuggestBot进行了离线分析（研究一），随后开展了为期三个月的对照实验（研究二）。结果显示，向用户呈现来自代表性不足主题的文章，可增加这些文章的工作量占比，且不会显著降低推荐内容的整体采纳率。我们讨论了研究结果的意义，包括说明忽视文章发现过程如何人为地缩小推荐范围。我们将此现象与常见的“过滤气泡”问题建立类比，以揭示任何采用推荐系统的平台都可能受到此问题的影响。