MAL was actually my source for thinking on this subject. In combination with the book Otaku: Database Animals[0] (anime fans catalogue the hell out of things, and this extends to tracking their anime and ratings) I realized you should be able to put together some very strong recommendations by scraping the MAL dataset — because the data should be fairly honest.
And then the realization that really the best recommendation isn’t to forge a new customized list altogether — it’s to simply find the most similar users and recommend items from their list. (MAL has/had a cosine similarity function for this, but no way to search because it’s basically an n^2 algorithm on 4M users; apparently they offered it at some point, and quickly found it untenable. That was what really kicked me off)
And then the realization that if I found users with similar taste, then shouldn’t they be friends? So then it becomes a MAL friendship algorithm..
Did a bunch of research on recommendation algorithms and weighting strategies, scraped most of the MAL users, stored it in a database, and then promptly procrastinated on actually implementing the algorithms. Been sitting on that for like 3 years now :|
