This use-case is about collaborative filtering. We are going to build a recommendation system based on embeddings created from a deep learning model. To do this, we are going to use the same dataset we used in Chapter 4, Training Deep Prediction Models, which is the retail transactional database. If you have not already downloaded the database, then go to the following link, https://www.dunnhumby.com/sourcefiles, and select Let’s Get Sort-of-Real. Select the option for the smallest dataset, titled All transactions for a randomly selected sample of 5,000 customers. Once you have read the terms and conditions and downloaded the dataset to your computer, unzip it into a directory called dunnhumby/in under the code folder. Ensure that the files are unzipped directly under this folder, and not a subdirectory, as you may have to copy...
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You're reading from R Deep Learning Essentials. - Second Edition
Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.
Read more about Mark Hodnett
Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.
Read more about Joshua F. Wiley
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Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.
Read more about Mark Hodnett
Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.
Read more about Joshua F. Wiley