承接前文：《机器学习在出版业的应用（一）》与《机器学习在出版业的应用（二）》。这是一篇笔者在计算机学习中作的作业文章，综合了目前一些机器学习在出版业可能的应用方向。目前，虽然数字出版如火如荼，图书数字化已是初具规模，但是真正的将现代科技应用到出版的却是不多。本文在此希望能够将机器学习这样新的现代技术引入出版这个古老的产业，也希望两者能碰撞出一些新的火花。本文为英文写作，由于篇幅比较长，所以分三篇全文摘抄于此。此文为（三）。
Machine Learning in Publishing Application（cont.）
4 Connecting the Publishers and Authors
As mentioned earlier, even a mega-bestseller can be rejected many times by editors in the beginning. For publishers, how to find a good author and how to keep in touch with authors is undoubtedly an important thing. In turn, it is also not easy for authors to find a suitable publisher to get their work published as soon as possible.
4.1 Find a good author
As stated in 3.2.3, there are some ways for publishers to determine if a manuscript has bestselling potential when they get it, especially if the author is a newcomer. Of course, publishers can find some good authors in this way, but each publisher may have its author base, so what is more important is how publishers can find the next best-selling author and his or her work in the publisher’s author base. Create author profiling, analyze the authors’ style, and refer to readers’ selection trends, it is possible to find the next possible best-selling author. Publishers can also use analysis to inform authors of trends in reader preferences, but this may affect the author’s original creation, the pros, and cons of which need to be weighed by both publishers and authors.
Further on author relationship management, is like customer relationship management. Machine learning in user relationship management has also been a series of research and results, here also does not repeat.
4.2 Find the right Publisher
In my previous work, I found that a large number of authors are very unfamiliar with publishers and related publishing organizations, most of the authors often know only a few well-known big publishers. As for the editors in the publishing house, and each editor and specialize in which areas, these are even more confused for them. Due to the branding effect, a large number of authors consider submitting their manuscripts to big publishers and famous editors, and they rarely consider these issues carefully as to whether the manuscripts are in line with the professional direction of the publishers or editors (many authors believe that editors are all-powerful in editing). However, it is not only famous publishers or famous editors who can make good books, these “celebrities” may be very busy or have too many manuscripts and overlook some good works. This further exacerbates the fact that many good works are not published successfully. So, how to provide detailed and right publishers’ and editors’ information to authors can be said to be a branch of the future development of the publishing industry.
Just as with book recommendations for the readers, a system for collecting publishers’ and editors’ information is needed here: what genres the publisher specializes in, how the books of the same genre have sold in the past for the publisher, what the editor is mainly specialized in, and what the past sales of the editor’s books are, etc. With the analysis of these data, then, we can recommend the right publisher and editor according to the author’s book genre, writing style, the length of the book, income, etc. Here for the analysis of data and the recommendation of publishers and editors, we can achieve this by machine learning related algorithms.
4 Conclusion
Since ancient times, publishing has been a technology-driven industry: papermaking, from woodblock printing to movable type, then now to electronic ink and digital publishing. We cannot predict what is the next technological change will be and when it will come, but what we know is that artificial intelligence is sweeping now, and artificial intelligence and deep learning have already penetrated and applied to the publishing field. Finally, let’s take another look at the title of the novel: When One Day a Computer Writes a Novel. So, what else is not possible? Machine learning may be just what traditional publishing needs.
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