Building, deploying, and optimizing recommender systems that effectively engages users and impacts business value, including revenue, is hard. Data scientists, machine learning engineers, and leads within global e-commerce, media, and on-demand domains have successfully designed, built, and deployed recommendation systems that impact business value. Download this paper to get insights, best practices, and advice from expert interviews and uncover how recommender systems teams handle preprocessing, feature engineering, training models, evaluating models, selecting which appropriate technologies to integrate, interoperability with open source, and more.
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Recommender Systems Best Practices
Learn insights from leaders and technical experts at global companies such as The New York Times, Tencent, Meituan, NVIDIA, and more.
