Mendeley’s Data Science team have been working to crack one of the hardest “big data” problems of all: How to recommend interesting articles that users might want to read? For the past six months they have been working to integrate 6 large data sets from 3 different platforms to create the basis for a recommender system. These data sets often contain tens of millions of records each, and represent different dimensions which can all be applied to the problem of understanding what a user is looking for, and providing them with a high-quality set of recommendations.
With the (quite literally) massive base data set in place, the team then tested over 50 different recommender algorithms against a “gold standard” (which was itself revised five times for the best possible accuracy). Over 500 experiments have been done to tweak our algorithms so they can deliver the best possible recommendations. The basic principle is to combine our vast knowledge of what users are storing in their Mendeley libraries, combined with the richness of the citation graph (courtesy of Scopus), with a predictive model that can be validated against what users actually did. The end result is a tailored set of recommendations for each user who has a minimum threshold of documents in their library.
We are happy to report that two successive rounds of qualitative user testing have indicated that 80% of our test users rated the quality of their tailored recommendations as “Very good” (43%) or “Good” (37%), which gives us confidence that the vast majority of Mendeley reference management users will receive high-quality recommendations that will save them time in discovering important papers they should be reading.
For those who are new to Mendeley, we have made it easy for you to get started and import your documents – simply drag-and-drop your papers, and get high-quality recommendations.
On our new “Suggest” page you’ll be getting improved article suggestions, driven by four different recommendation algorithms to support different scientific needs:
- Popular in your discipline – Shows you the seminal works, for all time, in your field
- Trending in your discipline – Shows you what articles are popular right now in your discipline
- Based on the last document in your library – Gives you articles similar to the one you just added
- Based on all the documents in your library – Provides the most tailored set of recommended articles by comparing the contents of your library with the contents of all other users on Mendeley.
Suggestions you receive will be frequently recalculated and tailored to you based on the contents of your library, making sure that there is always something new for you to discover. This is no insignificant task, as we are calculated over 25 million new recommendations with each iteration. This means that even if you don’t add new documents to your library, you will still get new recommendations based on the activity of other Mendeley users with libraries similar to yours.
To find your recommended articles, check out www.mendeley.com/suggest and begin the discover new papers in your field!