Many would agree: one of the biggest challenges (if not the biggest challenge) facing the ebook industry is discovery. With so many books published each year (professionally and through self-publishing services), it is becoming increasingly more difficult for readers to find what they are looking for in digital format. We all know that recommendation systems and their sophisticated algorithms point readers in the direction of the books they are likely seeking, but what if they could do more? What if the art of recommending books could be turned into pure science? What if no human brain was needed to tell us not just what books are related to one another but how their “sentiments” compare?
In this short video — a presentation held at the London Book Fair two weeks ago — Jim Bryant, Co-Founder and CEO for global distribution and book discovery network Trajectory, explains how using data science and machine learning efficiently (and creatively) is making digital content more discoverable than in the past.