Marlowe Recommends - Authors A.I.

A major step forward in helping readers find books they love

Since the launch of Authors A.I., our team of data scientists and authors has combined to produce the leading artificial intelligence to help authors write better books. 

Throughout this journey, we’ve been intent on using white-hat A.I. to give authors instant, accessible and affordable tools to improve their manuscripts. Our initial product, Marlowe, has always been designed as a virtual editing assistant that can be used by aspiring and accomplished authors, book editors, literary agencies and publishing houses. At the same time, we’ve cautioned against the misuse of generative A.I. to write novels, displacing the central role of the author in book publishing.

Now we’re excited to announce a new, related product: Marlowe Recommends. 

Marlowe Recommends was built for readers – including the customers of book publishers and book/audiobook retailers. Marlowe’s underlying code base has been extended to provide spot-on book recommendations called “comps.” 

Here’s how it works. 

‘If you liked this’ technology

Anyone who has used Amazon or Netflix is familiar with “if you liked this, you’ll like this” results, called collaborative filtering. In the book world, nearly all sites using this approach depend on metadata, sales data or buyer behavior – poor methods of how to arrive at a comp.

By contrast, Marlowe Recommends dissects many thousands of stories to identify the elements that draw in readers and listeners. Rather than relying upon secondary sources like metadata or sales information, our data scientists draw results from the stories themselves. The result is the most advanced book and audiobook comp technology in the world. 

Marlowe Recommends scores each book or audiobook on the “distance” between the book or audiobook and other books across a number of criteria starting with subject matter and writing style. From there, “match scores” can be derived and “distance markers” created to display other titles in the catalog that come closest to matching the original. 

Top 10 book comps to Fern Michaels's "Sara's Song."
Top 10 book comps to Fern Michaels’s “Sara’s Song.”

Ask us for a demo

That’s just the beginning of what Marlowe Recommends can do. We’ll be happy to demonstrate how Marlowe can be used to help your brand stand out from the pack. Please contact us for a demo.

Marlowe’s history

The algorithms behind Marlowe were created by Dr. Matthew Jockers, a world-renowned pioneer in the applications of machine learning to the study of fiction. While at Stanford University and the University of Nebraska, Jockers performed a multi-year computational analysis of more than 20,000 novels. The output of his work is found in his many articles and showcased in several of his books including Macroanalysis and his co-authored study of contemporary fiction, The Bestseller Code (St. Martin’s Press). 

In 2019, Jockers co-founded Authors A.I. where his academic research was applied and developed into Marlowe through the support of a team of engineers and data scientists. In 2021, Jockers was recruited by Apple as a distinguished research scientist.  He now manages the personalization science teams for books, video and podcasts. 

For more information, please contact:

Alessandra Torre
CEO, Authors A.I., Inc.
[email protected] 

JD Lasica
COO, Authors A.I., Inc.
[email protected]