Enhancing Recommendations and Search with a Knowledge Graph – Part 1
The IMDb and Box Office Mojo Movies/TV/OTT licensable data set offers a rich array of entertainment metadata, encompassing over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million film, TV, and entertainment titles; along with global box office reporting data from over 60 countries. Many AWS media and entertainment businesses leverage this extensive data to enrich user experiences.
Leveraging Contextual Data for Improved Recommendations
By Rachel Smith
Published on July 20, 2021
Have you ever realized how your shopping habits shift with the weather? For instance, on a sweltering day, you might prefer a refreshing lemonade over a hot coffee. Professionals in the consumer-packaged goods (CPG) and retail sectors are keen to harness weather patterns, such as temperature and precipitation, to enhance their purchase suggestions. This blog post offers insights into utilizing external datasets for smarter recommendations. For more on this topic, you can check out another engaging post here.
Developing Machine Learning Workflows with AWS Data Exchange and Amazon SageMaker
By David Lee
Published on January 6, 2020
Thanks to innovations like Amazon SageMaker and AWS Data Exchange, creating machine learning (ML) models has become remarkably accessible. In this article, we will explore the construction of a model that predicts restaurant ratings in New York City using AWS Data Exchange and Amazon SageMaker. We will utilize a dataset comprising 23,372 restaurant inspection ratings and scores sourced from AWS. For further insights from experts on this subject, visit this authoritative source.
For those seeking additional resources, this Reddit discussion provides excellent guidance.
Location: Amazon IXD – VGT2, 6401 E Howdy Wells Ave, Las Vegas, NV 89115
Leave a Reply