Ph.D. admissions committees, faculty search committees, etc. need to review dozens to hundreds of applications for a limited number of positions, and need to do so in a way that gives each applicant a fair review and keeps committee workloads manageable.

The way we’ve done this in a couple of committees I’ve been on is to implement two-stage review: each application is first read by two committee members, and if at least one of them thinks it merits further consideration, it moves to the next stage (full committee review, involving potential advisers, etc.). This requires us to assign those initial reviewers, however.

Effectively computing non-personalized recommendations can be annoyingly subtle. If we do naïve things like sorting by average rating, we get a top-N list dominated by items that one user really liked. Sorting by overall popularity doesn’t have this problem; as soon as we want contextual related-product recommendations, however (e.g. “people who bought this also bought”), and don’t want those recommendations to be dominated by the most popular items overall, the problem comes roaring back.

In my end-of-year blogging, I usually provide an update on my toolbox, and a review of the year.

So what’s happened? A quick (and likely incomplete) summary…

It’s that time of year again! Since I did a relatively full reset last year, for this year’s State of the Tools I’ll focus on changes and a few highlights. My Zoom studio post discusses my hardware and software for video recording in more detail.

Photo by Zalfa Imani on Unsplash

Inspired by Alan Said & Sole Pera’s posts on LinkedIn, here’s a quick summary of where you can find me and/or my work at RecSys next week in Bar, Italy: