A few days ago, William Gunn blogged about a fascinating idea for a paper recommendation engine and also described Mendeley’s role in it. His post then generated a lively discussion on FriendFeed.
Perhaps due to our relatively well-known affiliation with Last.fm, our idea for a research paper recommendation engine had always involved tags and collaborative filtering. But William brings up Pandora, another type of recommendation engine which doesn’t rely on critical mass, but on scoring music based on a certain set of dimensions.
So I was wondering, how feasible would such a human-scored recommendation engine be for research papers, and how could one do it? If one were to transplant the Pandora approach 1:1, one would have to find suitable dimensions on which to score papers – but what could those be? Epistemological position (e.g. positivist vs. constructivist), academic discipline, methods used? Or would you have to define a slightly different set of dimensions for each academic discipline? As opposed to music, where you can score tracks based on instrumentation, mood, tempo etc., I feel that it would be rather difficult to use this level of abstraction for research paper recommendations, but maybe I’m wrong.
Of course, you could think of tagging as a form of (binary) scoring, too, but without pre-defined dimensions. I thus remain convinced that tagging and collaborative filtering will be very good starting point for our recommendation engine. However, William’s suggestion made me think of an additional possibility.
Here’s what we might do: We have been planning to gradually add “Paper Pages” to the Mendeley site over the next few weeks. There will be one page for every paper in our database, containing the metadata, the abstract (if possible/available), some usage statistics about the paper, links to the publisher’s page (if available), and (later on) commenting functionality. We were also thinking about crowdsourcing approaches to enable users to correct mistakes in the metadata or merge duplicates.
Incorporating William’s suggestion, we could also give users the option to explicitly link paper pages to each other, and then say “this paper is related to this other paper because ___”. Two papers sharing the same tag may implicitly suggest a relation, but it might also be a case of a homonym – the same tag meaning two completely different things in different disciplines. An explicit link would solve this problem.
I didn’t have much time to fully think this through, and any further ideas would be appreciated!