Summary: In a comprehensive study of 18 recently presented DNN advancements in top-N recommenders, only 7 presented sufficient data to allow reproduction. Worse, of the 7 that could be reproduced none showed an actual improvement over simple linear and KNN techniques when those were properly optimized.
Added by William Vorhies on May 19, 2020 at 12:53pm — No Comments
Summary: In our recent article on “5 Types of Recommenders” we failed to mention Indicator-Based Recommenders. These have some unique features and ease of implementation that may be important in your selection of a recommender strategy.
A few weeks ago in the midst of our series on recommenders we published an article “5 Types of Recommenders” in which…Continue
Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance.
In our first article, “Understanding…Continue
Added by William Vorhies on January 31, 2017 at 9:30am — No Comments
Summary: There are many sources of packaged recommenders including the more comprehensive Digital Personalization platforms. It’s also possible to code your own. Here are a few things to consider.
Added by William Vorhies on January 24, 2017 at 2:00pm — No Comments
Summary: There are five basic styles of recommenders differentiated mostly by their core algorithms. You need to understand what’s going on inside the box in order to know if you’re truly optimizing this critical tool.
Summary: In this multi-part series we walk through the full landscape of Recommenders. In this article we cover business considerations as well as issues for Recommenders as a group. In the next articles we’ll discuss the details of the five major types of recommenders, improving their performance, and finally the coming impact of deep learning on Recommenders.