{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sequential-voting-promotes-collective","title":"Sequential Voting Promotes Collective Discovery in Social Recommendation Systems","arxiv_id":"1603.04466","date":"2016-03-14","proceeding":null,"authors":["L. Elisa Celis","Peter M. Krafft","Nathan Kobe"],"abstract":"One goal of online social recommendation systems is to harness the wisdom of\ncrowds in order to identify high quality content. Yet the sequential voting\nmechanisms that are commonly used by these systems are at odds with existing\ntheoretical and empirical literature on optimal aggregation. This literature\nsuggests that sequential voting will promote herding---the tendency for\nindividuals to copy the decisions of others around them---and hence lead to\nsuboptimal content recommendation. Is there a problem with our practice, or a\nproblem with our theory? Previous attempts at answering this question have been\nlimited by a lack of objective measurements of content quality. Quality is\ntypically defined endogenously as the popularity of content in absence of\nsocial influence. The flaw of this metric is its presupposition that the\npreferences of the crowd are aligned with underlying quality. Domains in which\ncontent quality can be defined exogenously and measured objectively are thus\nneeded in order to better assess the design choices of social recommendation\nsystems. In this work, we look to the domain of education, where content\nquality can be measured via how well students are able to learn from the\nmaterial presented to them. Through a behavioral experiment involving a\nsimulated massive open online course (MOOC) run on Amazon Mechanical Turk, we\nshow that sequential voting systems can surface better content than systems\nthat elicit independent votes.","url_abs":"http://arxiv.org/abs/1603.04466v1","url_pdf":"http://arxiv.org/pdf/1603.04466v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sequential-voting-promotes-collective","repo_url":"https://github.com/pkrafft/Sequential-Voting-Promotes-Collective-Discovery-in-Social-Recommendation-Systems","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}