It’s Not What A Book Says, It’s How It Makes You Feel “Boring, but a real problem to solve” Article after winning FutureBook BookTech Company of the year (thanks for the backhanded comment!) If you buy a TV, you’re interested in the viewing experience, the depth of color, the fluidity […]
Keywords / Deconstructing Amazon Search / Through The Dark Web After indulging in an attempt to find the holy grail of publishing, I pivoted to applying data science to solving a less lofty, but real need in publishing: keywords. The simple pitch was: There was a clear industry need to […]
The Holy Grail Of Publishing: Bestseller Prediction After validating that I could successfully classify novels into industry standard book categories (BISAC), I turned my focus back to the prize of predicting the next bestseller. I tackled this problem along two lines: Slush Filter In publishing, the flood of unsolicited manuscripts […]
I initially founded Kadaxis as a vehicle to experiment and validate hypotheses I had on applying machine learning models to book data. The goal was to see what models worked, then to wrap as a product and bring it to market (a little in reverse but I was intentional about […]
BISG Annual Meeting 2017 at The Harvard Club, NYC. This post originally appeared on the Kadaxis blog. I participated in a panel on “Rights, Metadata and Marketing” at the recent BISG 2017 Annual Meeting. Here are my responses to the questions I was asked: WHAT IS CURRENTLY WORKING FOR KADAXIS? Our approach […]
There’s been much recent conjecture on whether book sales can be predicted by text analysis alone. My company, Kadaxis, has dedicated the past few years to machine learning research and product development for the publishing industry. In our early days, we set out to build an algorithm to predict bestsellers, […]
