Benjamin Fields
Research Associate / Director of Data Science, Goldsmiths University of London/Fun & Plausible Solutions

Website | @alsothings

Ben leads Musicmetric’s data science team in an attempt to wrangle some sanity into the Internet’s vast supply of horribly formed music data. He has a PhD from the Intelligent Sound and Music Systems group in the Computing Department at Goldsmith University of London. His work there focused on merging social and acoustic similarity spaces to drive playlist creation and related user-facing systems. He is an expert on metadata, structured data, the semantic web and recommendation systems. In his spare time, he is a co-chair of the annual international Workshop On Music Recommendation And Discovery, has given an Ignite London talk about beer styles, occasionally DJs, is an accredited beer judge and homebrews beer. He thinks bios in the third person are weird but figures that’s how they’re meant to be written.


Data Science
Location: Room 1-6 Level: Intermediate
Benjamin Fields (Goldsmiths University of London/Fun & Plausible Solutions)
Average rating: ***..
(3.25, 4 ratings)
When constructing a music recommender system, which is more important: a musicological understanding of the catalog of music in a system or the number of times two particular songs were played one after the other and were `liked’? Even better, if a system knows the latter, does the former even matter? Do machines that predict behavior need to learn to listen? Or is observing behavior enough? Read more.


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