Fast Algorithms for Recommender Systems Agnihotri, Souparni
dc.contributor.department Electrical and Computer Engineering 2018-02-18T16:49:03.000 2020-07-07T05:12:02Z 2020-07-07T05:12:02Z 2017-04-11
dc.description.abstract <p>Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information from a large amount of data retrieved based on user preferences, interests or observed behavior about an item. The challenge is to come up with a fast and efficient algorithm that predicts user preferences on unseen items using a combination of content-based filtering (leveraging metadata about the users/items) and collaborative filtering (leveraging other user ratings). The algorithm uses a parallel computing framework called GPUFish to solve very large scale matrix completion problems. It addresses the main issue of predicting future ratings on a certain product, given a subset of user ratings. Such an algorithm is beneficial for large-scale content providers like Netflix and Amazon to filter their data in a fast and efficient manner and get accurate results. The testing of this algorithm is done through a food application which is implemented using JavaScript and a MongoDB database. The application prompts users to rate different food recipes and this information is used for predicting user ratings in the future. I am involved in developing the android application that uses the Volley networking library to interact with the database and have designed multiple screens to ease the process of user information retrieval.</p>
dc.identifier archive/
dc.identifier.articleid 1261
dc.identifier.contextkey 10453738
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath undergradresearch_symposium/2017/presentations/81
dc.relation.ispartofseries Symposium on Undergraduate Research and Creative Expression
dc.source.bitstream archive/|||Sat Jan 15 02:06:31 UTC 2022
dc.subject.disciplines Systems and Communications
dc.title Fast Algorithms for Recommender Systems
dc.type event
dc.type.genre event
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
relation.isSeriesOfPublication 6730f354-97b8-4408-bad3-7e5c3b2fca9d Computer Engineering (Agnihotri and Taylor)
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