Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms

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

The Evolution of SIGCHI conferences and the future of CHI

The ACM Conference on Human Factors in Computing Systems(CHI) was born in 1982 and has been held annually since 1985. Specialized conferences (such as IUI, CSCW, MobileHCI, and UIST) gradually emerged and have now become a significant part of the conference program that ACM SIGCHI offers the global HCI community

The new SIGCHI EC's values and strategic initiatives.

The SIGCHI EC has articulated the following 10 values. Specifically, these are instrumental values: They are our preferred methods of behavior. They are not an end goal, but they translate into a means by which an end goal is accomplished