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Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms

Houtti, Mo, Johnson, Isaac, Warncke-Wang, Morten, Terveen, Loren·2024·Proceedings of the International AAAI Conference on Web and Social Media
DOI 10.1609/icwsm.v18i1.31339
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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.

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Metadata

Authors
Houtti, M., Johnson, I., Warncke-Wang, M., & Terveen, L.
Venue
Proceedings of the International AAAI Conference on Web and Social Media
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Published
2024-05-28
Volume
18
Pages
624-636
ISSN
  • 2334-0770
  • 2162-3449
DOI
10.1609/icwsm.v18i1.31339
Cited by
2
References
2

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