Tag: Phil Gooch

Mendeley at JCDL 2014

Alice Bonasio
by Patrick Hochstenback @hochstenbach
Image by Patrick Hochstenback @hochstenbach

The Mendeley Data Science team have been busy attending some important events around the world. One of them has been JCDL 2014, the most prominent conference in the Digital Libraries arena. The conference looks at many of the problems we’re tackling at the moment, such as article recommendations and the best ways of automatically extracting information from research articles.

Maya Hristakeva, Senior Data Scientist at Mendeley, was particularly excited about the various approaches to topic modelling that were discussed at the event. “Topics were used as features for a diverse range of tasks, such as prediction of an author’s future citation counts, making personalised recommendations, search, author disambiguation, and creating more relevant citation networks, all features that make a direct impact to the research workflow on Mendeley.”

“We saw some really thought-provoking output come out of the JCDL14 proceedings such as Characterizing Scholar Popularity : A Case Study in the Computer Science Research Community. In JCDL’14” explains Kris Jack, Chief Data Scientist at Mendeley. “Some of the interesting research questions raised included one by Gonçalves, G. D., Figueiredo, F., Almeida, J. M., & Gonçalves, M. A. (2014) which asked whether it is possible to represent the popularity of a researcher using the number of readers that they have.”

It was also nice to see evidence in some of the papers presented that Mendeley readership is highly correlated with various measures of academic impact, such as h-index and publication venue importance,” says Mendeley Senior Data Scientist Phil Gooch.

Overall, this was a really valuable opportunity to connect with researchers who are working on similar problems to Mendeley, such as metadata extraction, recommendations, and citation/author/venue disambiguation, so we’re thinking about the idea of perhaps running an open challenge to focus this research into concrete output that could be of use in features for our users. If you have any ideas around that, do get in touch on Twitter with @_krisjack @mayahhf and @Phil_Gooch

Note: At Mendeley, we believe in dogfooding (it’s not as disgusting at it sounds, merely techy slang for using your own product to validate the qualities of that product…) so Maya, Kris and Phil took notes using Mendeley Desktop 🙂

 

Discussing the Future of Recommender Systems at RecSys2014

Alice Bonasio


Maya and Kris from the Mendeley Data Science team have just returned from RecSys2014, the most important conference in the Recommender System world. RecSys is remarkable in that it attracts an equal number of participants from industry and academia, many of whom are at the forefront of innovation in their fields.

The team had a chance to exchange perspectives and experiences with various researchers, scholars and practitioners.

“To me, it was encouraging to see how top companies across the world are investing in recommenders, as they are shown to enhance customer satisfaction and bring real value to both users and companies,” says Mendeley Senior Data Scientist Maya Hristakeva. “LinkedIn reported that 50% of the connections made in their social network come from their follower recommender, while Netflix says that if they can stop 1% of users from cancelling their subscription then that’s worth $500M a year, which of course justifies the fact they are investing $150M/year in their content recommendation team, consisting of 300 people.”

But one of the advantages of such a hybrid event is that it did not shy away from addressing the broader issues, such as how to ward against creating a “filter bubble” effect, how to preserve user’s privacy, and optimising systems for what really matters (and how this can be effectively defined). Daniel Tunkelang, LinkedIn, and Xavier Amatriain, Netflix, moderated a panel on “Controversial Questions About Personalization“, tackling some of these topics head on. Hector Garcia Molina from Stanford University also put forward the view that we’ll increasingly see a convergence of recommendations, search and advertising, despite noticeable scepticism from the attendees.

Kris Jack, Chief Data Scientist at Mendeley, says one of the main messages that he took away from the conference was the importance of winning a user’s trust in the early stages of using a recommender system.

“The best systems have been shown to start off by providing recommendations that can quickly be evaluated by users as being useful before gradually introducing more novel recommendations. So in the case of helping researchers to find relevant articles to read, it’s probably best to start by recommending well known but important articles in their field, before recommending some less well known but very pertinent articles to their specific problem domain.” explains Kris. “Other important factors include reranking (the order in which recommendations should be shown), the UI design that can best support interaction with the recommender system, and the ways in which we can build context-aware recommendations.”

What do you think of the current recommendation features on Mendeley? Are there any particular ones that you’d like to see implemented? Would you like to join the team and work on making them even better? Let us know in the comments below, or Tweet the team directly @_krisjack @mayahhf and @Phil_Gooch .If you’re interested in finding out more about what the Data Science Team is developing in that arena, you can also watch their Mendeley Open Day presentation here.