[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-306025-105":53,"doc-detail-306025-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","folk-theories-and-user-strategies-on-dating-apps-algorithmic-matchmaking-experience","Folk Theories and User Strategies on Dating Apps - Algorithmic Matchmaking Experience","","The paper investigates how dating-app users experience and manage algorithmic matchmaking by surfacing the folk theories and practical strategies they develop to improve success. Prior work often focuses on explicit algorithmic pairing via compatibility scores, but newer apps use background filtering without clear user visibility. The study identifies two main user behaviors: boosting an “attractiveness score” to match broadly and “teaching” the algorithm by adjusting actions based on perceived mismatch. Findings extend folk-theory formation research and raise practical and ethical questions about algorithmic intervention in romantic and sexual preferences and behavior.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/folk-theories-and-user-strategies-on-dating-apps-algorithmic-matchmaking-experience/306025/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/folk-theories-and-user-strategies-on-dating-apps-algorithmic-matchmaking-experience/306025.png","ImageObject",442,249,{"name":88,"@type":89},"Eliana","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-20","2026-09-19",true,{"@type":98,"interactionType":99,"userInteractionCount":73},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What does the paper aim to understand about dating-app users?","Question",{"text":108,"@type":109},"It examines users’ experience with algorithmic filtering by identifying the folk theories and strategies they use to maximize dating success.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How do the study’s findings describe users’ typical strategies?",{"text":113,"@type":109},"Some users try to raise their “attractiveness score” to match with more people, while others attempt to influence the algorithm to better reflect their unique preferences.",{"name":115,"@type":106,"acceptedAnswer":116},"Why does the paper argue that algorithmic filtering matters to romantic and sexual behavior?",{"text":117,"@type":109},"Because filtering shapes which profiles users can view and match with, yet users often cannot tell how background algorithms mediate interactions, prompting theory-building and strategy development.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},306025,1789832150,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":73,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":20,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":125,"read_time":47},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","Folk Theories and User Strategies on Dating Apps  \nHow Users Understand and Manage Their Experience with Algorithmic Matchmaking  \nKarim Nader 1(B)  and Min Kyung Lee2   \n1 Department of Philosophy, The University of Texas at Austin, Austin, TX 78705, USA  \n[karim.nader@utexas.edu](karim.nader@utexas.edu)  \n2 School of Information, The University of Texas at Austin, Austin, TX 78705, USA  \n[minkyung.lee@austin.utexas.edu](minkyung.lee@austin.utexas.edu)  \nAbstract. The goal of this paper is to understand the experience of users with algorithmic filtering on dating apps by identifying folk theories and strategies that users employ to maximize their success. The research on dating apps so far has narrowly focused on what we call algorithmic pairing–an explicit pairing of two users together through a displayed compatibility score. However, algorithms behind more recent dating apps work in the background and it is not clear to the user if and how algorithmic filtering is mediating their interaction with other users. This study identifies user goals and behaviors specific to dating apps that use algorithmic filtering: while some users employ various strategies to boost their“attractiveness score” to match with as many people as possible, others attempt to teach the algorithm about their unique preferences if they believe that the filtering is not working in their favor. Our research adds to the growing literature on folk theory formation by introducing dating apps as a novel context for research. Since folk theories are developed with specific goals in mind, they reveal user concerns around algorithmic filtering. Our hope is that this paper starts a conversation on the practical and ethical question of algorithmic intervention on sexual and romantic preferences and behavior.  \nKeywords: Algorithmic filtering · Dating apps · Human-AI interaction · Folk theories · User experience  \n1 Introduction  \nWith the success of recommender systems on platforms for online shopping and streaming services, dating apps are using similar methods to filter and recommend users to one another. As most new couples in the United States meet online [1, 2], algorithmic filtering is shaping romantic and sexual relationships by influencing the profiles a dating app user can see and match with. This paper explores how users respond to the invisible algorithm that is affecting their dating life.  \n© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  \nM. Smits (Ed.): iConference 2022, LNCS 13192, pp. 445–458, 2022 .  \n[https://doi.org/10.1007/978-3-030-96957-8](https://doi.org/10.1007/978-3-030-96957-8_37)[_](https://doi.org/10.1007/978-3-030-96957-8_37)[37](https://doi.org/10.1007/978-3-030-96957-8_37)  \n446 K. Nader and M. K. Lee  \nThe promise of algorithmic matchmaking rests on the assumption that romantic and sexual preferences can be predicted. Some dating apps give users explicit compatibility scores. For example, eHarmony uses its 32 Dimensions of Compatibility to match users together. They claim that they use scientific research to determine dating behavior [3] . OkCupid uses a Match % to help user identify potential partners [4] . Other dating apps keep their algorithm in the background and give the user no indication of their compatibility with others. Apps like Tinder and Bumble pre-selects the profiles that a user can browse through by filtering them based on compatibility [5] . However, the user is not told that the profiles that they see are selected by an algorithm.  \nOutside of dating apps, recommender systems have been extremely effective in affecting user behavior and preferences [6] . “At Netflix, 2/3 of the movies watched are recommended; at Google, news recommendations improved click-through rate by 38%; and for Amazon, 35% of sales come from recommendations” [7] . However, this level of intervention has also led users to develop strategies to circumvent algorithmic control: for examples, workers use workarounds to avoid undesira","cbCaihQDrDeABARP","https://ap.wps.com/l/cbCaihQDrDeABARP","pdf",239524,"English","# Introduction\n## Algorithmic Pairing and Algorithmic Filtering","[{\"question\":\"What does the paper aim to understand about dating-app users?\",\"answer\":\"It examines users’ experience with algorithmic filtering by identifying the folk theories and strategies they use to maximize dating success.\"},{\"question\":\"How do the study’s findings describe users’ typical strategies?\",\"answer\":\"Some users try to raise their “attractiveness score” to match with more people, while others attempt to influence the algorithm to better reflect their unique preferences.\"},{\"question\":\"Why does the paper argue that algorithmic filtering matters to romantic and sexual behavior?\",\"answer\":\"Because filtering shapes which profiles users can view and match with, yet users often cannot tell how background algorithms mediate interactions, prompting theory-building and strategy development.\"}]","Folk Theories and User Strategies on Dating Apps - Algorithmic Matchmaking Experience | PDF"]