Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms

TL;DR

This paper investigates how recommender systems can address content gaps in peer production platforms like Wikipedia, demonstrating that promoting underrepresented topics increases work diversity without harming overall engagement.

Contribution

It provides empirical evidence that recommending underrepresented topics enhances content diversity without reducing user engagement, informing platform design strategies.

Findings

Increased work on underrepresented topics

No significant reduction in overall recommendation uptake

Implications for recommendation diversity in peer production

Abstract

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.