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dc.contributor.advisorAlexander 'Sandy' Pentland.en_US
dc.contributor.authorHong, Christie.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-09-15T21:56:28Z
dc.date.available2020-09-15T21:56:28Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127409
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 59-62).en_US
dc.description.abstractRecommender systems are all-pervasive on every online medium we interact with today. Yet, they impose a substantial problem of homogenization of users over time, leading to lack of visibility of the discriminated. This problem stems from the fundamental literature and traditional approaches around recommender systems and the primary focus on user-centric accuracy. In practice, this results in popularity bias, filter bubbles and "down the rabbit holes," where popular content in "your circle" continues to be recommended, and less popular content becomes discriminated against. And how does one measure popularity? Typically by some data metric of ratings: likes on a post, views on a video, the number of 5 star ratings. This ultimately results in locally optimal recommendations, where the users are content and comfortable with taking these results, but globally sub-optimal diversity, as the ecology of users grows more and more homogeneous over time. In this paper, I propose an approach to de-homogenizing recommender systems via an ecological values-based recommender system, as well as different metrics to evaluate on. This system encompasses both personalization, as well as diversification at both a user, and ecosystem of users, level. With this research, I demonstrate that this approach can introduce diversity in applicable ways, and reveals the weaknesses in current traditional models in tackling the problem of homogeneity. These insights can be used to guide future recommender systems and continue the conversation of developing more diverse and impactful recommendations.en_US
dc.description.statementofresponsibilityby Christie Hong.en_US
dc.format.extent62 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAn Approach to de-homogenizing recommender systemsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1192560936en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-09-15T21:56:27Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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