Recommendation systems are increasingly important in our digital world, and many organizations are looking to machine learning to create recommendations that leverage their unique datasets to provide better results. Most applications have used techniques best described as wide learning: logistic regression, minhash, and weighted alternated least squares (WALS). As part of this trend, many leading digital companies are using deep learning to create recommendation models, simplifying feature engineering and resulting in improved performance.
Tim Seears and Karthik Bharadwaj Thirumalai explain how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest using embeddings. They then demonstrate how to extend this with WALS matrix factorization to achieve wide and deep learning—a process which is now used in production for the Google Play Store.
Tim Seears is area practice director for Asia-Pacific at Think Big, a Teradata company. Previously he was CTO of Big Data Partnership (acquired by Teradata in 2016), which he cofounded after a career spent in the space industry working on NASA’s Cassini orbiter mission at Saturn. Tim and his team established Big Data Partnership as a dominant thought leader throughout the European market, providing data science, data engineering, and big data architecture services to global enterprise customers.
Karthik Bharadwaj is a senior data scientist in the Data Science Center of Expertise at Teradata, where he provides analytic thought leadership and generating demand for Teradata products. Karthik has seven years of experience in working in the data management and analytics industry. Previously, he worked as a researcher at IBM Research to develop smarter transportation systems that predict traffic on the Singapore road network. Karthik holds a master’s degree from the National University of Singapore.
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