BioMedExperts January 13, 2008Posted by Andre Vellino in CISTI Visualization, Citation, Information retrieval, Recommender, Search, Social networks.
Whatever “social networking site for scientists” means exactly, I’m not sure, but whatever it is, it comes in many flavours. There’s the “Facebook” / “LinkedIn” kind of site like Nature’s with forms, blogs, people with whom to make connections etc. There’s the “Del.icio.ous” / “Connotea”, bookmark-centric kind like Elsevier’s 2collab and there’s the “Google Scholar” type of search-engine, like GoPubMed that has been enhanced with subject-specific capabilities such as MeSH and GeneOntology lexicons to improve relevance and classification. GoPubMed also features the ability to search for authors (e.g. by frequency of publication) and Journal (e.g. by impact factor.)
One of the business analysts at CISTI (Naomi Krym) pointed me to the recent launch of BioMedExperts – a new social networking site for bio-med scientists. It was developed by Collexis (and Dell, which supplied the hardware) and combines large subsets of the functionalities in the above services. You can define your own publishing profile as an author, invite authors to your network, define your academic profile and so forth. Collexis also offers “context sensitive search”, whose search results, like GoPubMed, are driven by biomed ontologies.
What I like most about BioMedExperts is the UI that Collexis has devised to help the user navigate the huge network of authors from a citation network. Here’s what the applet looks like:
The goal of Recommender Systems is sometimes framed as “give me what I want” vs. “give me the tools to explore the space so that I can find what I want”. The Collexis applet does an interesting job of the latter for authors and citations.
However, using this applet for even a few minutes demonstrates the need for automated tools that also “recommend” (in some generic sense) or at least removes or hides irrelevant information. Without some kind of recommendation capability there’s just too much data to display in such a small area: it needs to be condensed somehow. Given the appropriate controls (e.g. the slider bars at the top of the Collexis applet) a recommender system could show you a range between the Top N recommendations and the “long tail” in the space of possible recommendations.
So what are the “appropriate contols” for a recommender? Well, it depends on the space of objects being recommended and the recommender algorithm(s). For authors, for example, one of the slider bars could be the weighting given to the text-content similarity of other authors’ articles. Another slider bar could control the display by the similarity of authors’ citation patterns.
For recommending articles with collaborative filtering – e.g. from implicit ratings from users’ viewing patterns – a slider control could weight the articles that are most similar by usage (in different time windows) or by users’ explicit ratings (e.g. “innovation” / “information” / “authority”.)
We’re still not quite there yet, but I think that something like Collexis’ applet is a promising interface for navigating recommendations.