I don't see how that's clickbaity, or unrelated to machine learning. No, it's not a technical post, but it still raises an interesting point about what recommendation systems are actually optimizing for and what's missed.
Regarding “clickbaity”, I clicked it hoping to hear about some interesting way to apply the concept of depth first search to machine learning. The actual subject, however, has nothing to do with depth first search: depth vs. breadth first has to do with the order in which links are traversed, but what the author wants is for machine learning to discover links it currently doesn’t know about at all (i.e. explicit textual references). However, this probably isn’t intentional clickbait; the author probably thought the analogy made sense. (Maybe it does somehow and I’m just not seeing it.)
I see it as the following, he views breadth first as just skimming over each book, seeing the books that are related to it when sitting side by side (perhaps when in another users cart), as opposed to depth, which goes into each item, and then goes deeper picking out books from within that book.
From what I understand, the author needs to brush up his knowledge about depth first search. Scanning the book for references to other books would normally be a part of breadth first search. Also, a bfs would yield books that are more related to the selected book than a DFS.
but those books this new method finds might not be books that customers are likely to buy, even with the new information. perhaps they are just bad books but are still referenced in such a way.
Additionally, this sort of referential database could be quite useful in other pursuits. Want to map mentions within works to research how culture addresses itself? It may also capture a larger market segments, people who are interested in one topic may find they share interests with the author in another topic.