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

Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome

The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. Video Abstrac

Erratum: Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome (Cell (2020) 182(6) (1460–1473.e17), (S0092867420309983), (10.1016/j.cell.2020.08.007))

(Cell 182, 1460–1473.e1–e17; September 17, 2020) In preparing the final version of this article, we overlooked some errors and we apologize for these shortcomings. None of these errors involved our data analyses or affected the conclusions presented in the manuscript. These errors have now been corrected online. 1. In Figure 2B the y axis should read “log10 (mg/gram tissue),” not “log10 (mg/gram stool)” as it was labeled originally.2. In Figure 4, panel D was mistakenly labeled as panel “E,” while panel E was mistakenly labeled as panel “D.”3. In the section titled “Microbiome and Metabolome Data Integrated with Transcriptomic and Epigenetic Differences Reveal Novel Host-Microbiome Interactions in IBS,” we inadvertently wrote “additional gene-transcript and gene-metabolite associations” which should have been be “gene-microbe and gene-metabolite associations.”4. In the methods section entitled “Multi-omics data integration” we sincorrectly stated outputs from Lasso and stability selection models were inspected and filtered at FDR &lt; 0.1 which should have been FDR &lt; 0.25.5. Finally, the legend for Figure S4F should read “hypoxanthine is consistently lower in IBS-C and IBS-D,” instead of “hypoxanthine is consistently lower in IBS-C and IBS-C.” [Figure presented] [Figure presented] [Figure presented] [Figure presented]</p