PubMed’s MiSearch June 12, 2008Posted by Andre Vellino in Digital library, Recommender service.
MiSearch uses implicit profiling in a way that bears some resemblance to what I think an article recommender should do. However, instead of “recommending”, it uses a content-based algorithm to re-ranking search results. From the help file:
MiSearch uses a classification algorithm based on MeSH term, substance names and author names associated with citations. Two sets are defined. One is the set of articles you have previously clicked on to view. The other is all of PubMed. For each citation in the retrieval set, the algorithm calculates the likelihood that the citation is a member of these two sets. Article having the highest likelihood of belonging to the set of articles you have viewed are ranked at the top of the list.
And from the paper mentioned above:
MiSearch is using query expansion with probabilistic weighting of terms derived from the implicitly defined relevant document set.
My concern with re-ranking search results in this way is that it isn’t clear why the re-ranking has happened in the way it has or, indeed, how the user can control it. Explanations and user-interfaces that provide them are key to the usability of such systems, I think.