Product recommendation system using ML

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dc.contributor.authorDange, Anas
dc.contributor.authorBaig, Sahil (19CO49)
dc.contributor.authorSirkazi, Ruzbihan (19CO48)
dc.contributor.authorMohd., Aasif (19CO36)
dc.contributor.authorUlde, Kais (19CO46)
dc.date.accessioned2023-06-12T05:58:17Z
dc.date.available2023-06-12T05:58:17Z
dc.date.issued2023-05
dc.description.abstractProduct Recommendation System Using ML In today’s modern epoch of information technology, the idea of efficiently finding one’s favourite product in a large dataset of application database, becomes an essential issue to address for the online content providers in order to attract the masses as opposed to their competitors. Recommender systems or recommendation systems, as they are popularly known, are information filtering systems which are usually integrated with several consumer and com- mercial applications. Such systems act as a bridge between various content facilitators such as social media websites, e-commerce portals, streaming platforms, etc. and the users of these applications, by suggesting them items from the application database which conform to the user preferences and past activities. Such personalized systems play a vital role, especially when the user is unclear of the item to the searched for. These systems are infiltrating every aspect of our lives, in the form of ?Because you watched? header on Netflix, ?People you may know? section on Facebook, ?Customers who bought this also bought’ partition on Amazon.en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/4119
dc.language.isoenen_US
dc.publisherAIKTCen_US
dc.relation.ispartofseriesPE0730;
dc.subjectProject Report - COen_US
dc.titleProduct recommendation system using MLen_US
dc.typeProject Reporten_US
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