I'm looking to write a basic recommendation engine that will take and store a list of numeric IDs (which relate to books), compare those to other users with a high volume of identical IDs and recommend additional books based on those finds.
After a bit of Googling, I've found this article, which discusses an implementation of a Slope One algorithm, but seems to rely on users rating the items being compared. Ideally, I'd like to achieve this without the need for users to provide ratings. I'm assuming that if the user has this book in their collection, they are fond of it.
While it strikes me that I could default a rating of 10 for each book, I'm wondering if there's a more efficient algorithm I could be using. Ideally I'd like to calculate these recommendations on the fly (avoiding batch calculation). Any suggestions would be appreciated.