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Bellarena- deep learning based personalized online women's clothing recommendation system

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dc.contributor.author O.D.C, Bulathsinghala,
dc.date.accessioned 2023-08-02T09:40:48Z
dc.date.available 2023-08-02T09:40:48Z
dc.date.issued 2020
dc.identifier.citation Bulathsinghala, O.D.C (2021) Bellarena- deep learning based personalized online women's clothing recommendation system. BEng. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 2016109
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/1588
dc.description.abstract BELLERENA is designed to give more accurate and personalized recommendations for online shoppers. With the large amount of information which comes rapidly to the internet, makes it difficult in finding needful information. This becomes a critical problem for internet users in finding needed information or products in any platform. As a solution expert started inventing and developing various kinds of recommendation systems. And the Collaborative filtering approach has become the most important algorithm among other algorithms. With the use of collaborative filtering algorithms, it’s easy to make quality recommendations. E companies also started using recommendation systems to give suggestions for the products, which their customers might be interested on. Recommender systems provide excellent sales for E-commerce companies. Recommender systems were commonly used to recommend items such as Movies, Books, Items, Music etc. to customers. Netflix uses recommender systems in recommending movies and Amazon uses the recommendation system in recommending books to their customers. There are several ways of making recommendations, such as Providing the top list items, making recommendations based on demographic data, analyzing the past interaction activities of users. Among all, the collaborative filtering approach is the best approach invented by [Goldberg et al., 1992]. There are three types of recommendation systems, Collaborative Filtering, Content-based recommender system and Hybrid approach. The collaborative filtering can be divided into Model based filtering and Memory based filtering, under model-based filtering comes the Matrix factorization, Clustering, Bayesian networks. And under Memory based filtering comes the Item – Item collaborative filtering approach and User- User collaborative filtering approach. Rather than making common recommendations for the users BELLERENA is designed to give more accurate and personalized recommendations for online clothing shoppers. Since online clothing is a massive area, BELLERENA is scoped down to Women’s online clothing personalized recommendations. In order to personalize the en_US
dc.language.iso en en_US
dc.publisher IIT en_US
dc.title Bellarena- deep learning based personalized online women's clothing recommendation system en_US
dc.type Thesis en_US


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