Data: Surfacing the Academic Long Tail (SALT)
You don't necessarily need a significant backlog of data to make this work locally. Yes, we had ten years worth from JRUL, which turned out to be a vast amount of data to crunch. But interestingly in our testing phases when we worked with only 5 weeks of data, the recommendations were remarkably good. Of course, whether this is true elsewhere, depends on the nature and size of the institution. But it’s certainly worth investigating.
If the API is to work on the shared service level, then we need more (but potentially not many more) representative libraries to aggregate data from in order to ensure that recommendations aren't skewed to represent one institution’s holdings, course listings or niche research interests, and can support different use cases (i.e. learning and teaching).