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How TrendMD uses collaborative filtering to show relatedness

TrendMD is (as its website states) “a content recommendation engine for scholarly publishers, which powers personalized recommendations for thousands of sites”. An interesting blog post by Matt Cockerill of TrendMD (published February 2016) claims “TrendMD’s collaborative filtering engine improves clickthrough rates 272% compared to a standard ‘similar article’ algorithm in an A/B trial”. That sounds pretty impressive.

The truth about students and search?

The truth about search seems to be more astonishing than anything you could imagine. Lin Lin, EBSCO Senior User Experience Researcher, talking at the UKeiG Annual Meeting last week, provided some startling revelations, drawing on EBSCO’s wide experience of observing search behaviour with students ranging from age seven to postgraduate - they should know, since they claim to have the largest user research team in the industry, So what is the reality of student search?

A day in the Life of a (Serious) Researcher

 

How do researchers really look for and find content for their research? That’s a pretty fundamental question! So I turned to the research project “A Day in the Life of a (Serious) Researcher” with great anticipation to identify that part of the researcher activity relating to seeking and finding information. I found the survey exciting but at the same time questionable in some of its conclusions.

How many answers would you prefer ?

“We find users prefer one answer.” This was the comment of Google’s Behshad Behzadi when presenting Google’s new Ultimate Assistant. In case you don’t already know, Google’s Ultimate Assistant will answer your questions, whether you key them in or (in Google’s opinion the most likely) you speak to the device. Most of Behzadi's presentation was based around his smartphone, not using the desktop at all. What kind of questions?

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