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Search Results for recommendation transparency

RecSys 2011 Tutorial: Recommendations as a Conversation with the User

October 31st, 2011 · 4 Comments

  Last week, I had the privilege to present a tutorial at the 5th ACM International Conference on Recommender Systems (RecSys 2011). Given my passion for HCIR and my advocacy for transparency in recommender systems, it shouldn’t surprise regular readers that I focused on both. Unfortunately the tutorial was not recorded, but I hope the […]

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Tags: General

Transparency or FAIL

April 13th, 2009 · 4 Comments

I’ve long been proponent of transparency in search engines and recommendation systems, on the grounds that transparency cultivates trust even in the face of the inevitable fallibility of algorithmic models. Perhaps my stance has an ideological tinge. But, as we’ve seen from recent events, transparency isn’t just an academic concern. I’d like to touch on […]

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Tags: General

How Recommendation Engines Quash Diversity

February 24th, 2009 · 16 Comments

As regular readers here know, I have strong opinions about how recommendation engines should work. So does Daniel Lemire, a regular reader who specifically argues in favor of diversity in recommender systems. Well, this post is for him and all who share his concern. In “Does Everything Really Sound Like Coldplay?“, Vegard Sandvold explains: When […]

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Tags: Uncategorized

SIGIR 2010: Day 1 Technical Sessions

July 21st, 2010 · 3 Comments

I’ve always felt that parallel conference sessions are designed to optimize for anticipated regret, and SIGIR 2010 is no exception. I decided that I’d try to attend whole sessions rather than shuttle between them. I started by attending the descriptively titled “Applications I” session. Jinyoung Kim of UMass presented joint work with Bruce Croft on […]

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Tags: General

The Napoleon Dynamite Problem

November 21st, 2008 · 15 Comments

This week’s New York Times Magazine features an article by Clive Thompson about the Netflix Prize. The Netflix Prize, sponsored by the Netflix movie rental company, is perhaps the best marketing stunt I’ve seen in the history of machine learning: The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone […]

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Tags: General

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