Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/7645
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMahajan, Vinamr-
dc.contributor.authorSandhu, Rajinder [Guided by]-
dc.date.accessioned2022-10-11T10:13:07Z-
dc.date.available2022-10-11T10:13:07Z-
dc.date.issued2019-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/7645-
dc.description.abstractRecommender frameworks are an intriguing issue in this period of massive information and web showcasing. Shopping on the web is omnipresent, however online stores, while prominently accessible, come up short on indistinguishable perusing alternatives from the physical assortment. Online stores regularly offer a perusing alternative, and even permit perusing by genre, yet frequently the quantity of choices accessible is still overpowering. Business sites endeavor to balance this over-burden by presenting exceptional deals, new choices, and staff favorites, however the best showcasing angle is to suggest things that the client is probably going to appreciate or require. Unless online stores need to procure mystics, they need another innovation. “Recommender systems are systems that based on information about a user's past patterns and consumption patterns in general, recommend new items to the user.”en_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectRecommender systemsen_US
dc.subjectRecommender frameworksen_US
dc.titleRecommender Systemsen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

Files in This Item:
File Description SizeFormat 
Recommender Systems.pdf1.45 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.