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 and minhash and weighted alternated least squares (WALS). In the last two years, deep learning applications have been put into production, and many leading digital companies are using deep learning to create recommendation models, simplifying feature engineering and improving performance.
Junxia Li and Mo Patel demonstrate how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest for recommendations using embeddings. You’ll also learn how to achieve wide and deep learning with WALS matrix factorization—now used in production for the Google Play store. Focusing on memorization and generalization enables you to build machine learning models that serve both fine- and coarse-grained objectives.
Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.
Junxia Li is a senior data scientist at Think Big Analytics, a Teradata company, where she focuses on solving recommendation problems using the latest techniques like deep and wide learning across clients from several verticals. Junxia has successfully implemented advanced machine learning models for clients from a number of different industries, such as automotive, telecommunications, and retail. Junxia is enthusiastic about emerging advancements in machine learning, especially deep learning and AI, and she enjoys reading cutting-edge research papers and experimenting with new ideas. She holds a master’s degree in business and IT and a dual bachelor’s degree in economics and information systems.
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