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GoCars Personalised Hybrid Recommender System

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dc.contributor.advisor Vidanage, Kaneeka
dc.contributor.author Fernando, Nelaka Senura
dc.date.accessioned 2019-02-19T08:53:34Z
dc.date.available 2019-02-19T08:53:34Z
dc.date.issued 2018
dc.identifier.citation Fernando, N. S. (2018) GoCars Personalised Hybrid Recommender System. BSc. Dissertation. Informatics Institute of Technology en_US
dc.identifier.other 2014012
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/114
dc.description.abstract Nowadays, the internet is being widely used for daily shopping purposes. Shoppers often make their purchases on the web more than going to the store, with the expanding E-commerce and web-based shopping, the Personalised recommendation has become a necessity in a domain such as automobiles. since the majority of automobile web sites based on filtering based approach, enable Personalised recommender would be invaluable for consumers with little to no knowledge about the automotive industry. However, a fitting personalized recommender system into automotive websites could be a great asset to automobile dealers and end consumers. This is an innovative way of optimizing car recommendation system research done in Sri Lankan region. en_US
dc.subject Matrix Factorization en_US
dc.subject Machine Learning en_US
dc.subject Collaborative Filtering en_US
dc.title GoCars Personalised Hybrid Recommender System en_US
dc.type Thesis en_US


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