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Recommender systems are algorithms that suggest relevant items to users based on data. They generate large revenue for the modern e-commerce industry. 35% of Amazon web sales were generated through their recommended items [source: McKinsey]. This study aims to construct an apparel recommender system for Amazon users through user-rating history, product images and product title text. Multiple deep learning models were built on both readily-available and engineered datasets resulting in a multi-step recommender system. Tableau and a web app are used to display results, along with evaluation measurements. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Baek, Janghyun; Tsai, John; Shamoun, Justin; Marable, Muriel; Cui, Ying; Altintas, Ilkay; McAuley, Julian (2020). Amazon Recommender System. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J08C9TSC
Type
dataset
Identifier
ark:/20775/bb8503744c
Language
English
Subject
Collaborative filtering Data Science & Engineering Master of Advanced Study (DSE MAS) Word2vec Deep learning Recommender system Machine learning Convolutional Neural Network (CNN) Recommender engine Graph database Neo4j Natural Language Programming (NLP) DSE MAS - 2020 Cohort
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