Lessons for article recommendation services


Today someone proposed the creation of a sub-reddit where scientists could recommend papers to each other. While it's a nice thought, I can almost guarantee that it's going to be a failed effort. There are already sites like Faculty of 1000, which try to use panels of experts to recommend good papers. In my experience, they mostly fail at listing things that I want to read.

The main reason such sites are useless is that we scientists are uber-specialized, so what you think is the greatest paper ever will likely have very litle interest for me. It's not that I don't want to read about cool discoveries in other fields, it's just that I don't have time to. Until they invent the matrix-esque brain-jack for rapid learning, I have to prioritize my time, and my field and my work will always come first.

There are only two systems I've found that work well. The first are recommendation systems based on what you've read in the past, and what your colleagues are reading. CiteULike, for example, recommends users that have bookmarked similar papers to you, and perusing through their libraries gives me an excellent source of material. The other quality source of recommendations is FriendFeed, where I can subscribe to the feeds of other bioinformaticians with similar interests, and we can swap links to papers and comments about those papers.

Both of these systems are all about building micro-communities, with a focus that you can't achieve in larger communities like Reddit. In this way, it's sort of like a decentralized version of departmental journal clubs, or specialized scientific conferences. Any site that ignores the value of creating this type of community is pretty much doomed to failure from the start.

Comments

Written by Andre Vellino -

Chris - I'd be interested to know what you thought of my experimental article recommender on CISTI Lab_ http_//lab.cisti-icist.nrc-cnrc.gc.ca/synthese/welcome.jsp Because of the "sparsity" problem (not enough scientists with similar enough reading profiles), the system is seeded with "fake users" whose "preferences" are the citations in the article collection. You can read more about how it works here_ http_//lab.cisti-icist.nrc-cnrc.gc.ca/cistilabswiki/index.php/Synthese_Recommender

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