Hi Everybody,
I just ran an experiment that surprised me and I thought folks on this list would find interesting.
*tl;dr* We found that navigation vector embeddings for articles (as produced by Ellery Wulcyzn https://meta.wikimedia.org/wiki/Research:Wikipedia_Navigation_Vectors) outperform content-based vector embeddings (word2vec on article text) by 62% vs 37% accuracy in a task-based user study. I've volunteered to help with the engineering to productionize navigation embedding https://phabricator.wikimedia.org/T158972 and this study reinforces my eagerness to get navigation vectors out in the world!
*More detail: *The maps we use in Cartograph (cartograph.info) are almost entirely built on "embedding" vectors for articles. We experimented with two word2vec-based embeddings: *content vectors* mined from article text and link structure, and* navigation vectors* mined from user browsing sessions. For the latter, we used Ellery Wulczyn's navigation vectors https://meta.wikimedia.org/wiki/Research:Wikipedia_Navigation_Vectors. By staring at maps, our intuition told us that the navigation vectors seemed better in "preference spaces" where the human taste space wasn't necessarily easily encoded into Wikipedia text.
Last weekend we ran a Mechanical Turk experiment to test this intuition. We created two Cartograph maps of movies: one built on navigation vectors and one built on content vectors. We identified 40 relatively popular movies that were not close neighbors in either map (i.e. cities that were not too close to each other) and ran a Mechanical Turk study using the maps.
For each Turker, we randomly selected 5 seen movies (out of the 30), and asked them to evaluate maps for each movie. For each movie city, we showed the map region around the city, but hid the city and asked them to guess the city from a list of 12 movies they had seen (screenshot below). We added in trivial validation questions using sequels to ensure Turkers were working in good faith (show a map for "Rocky II" that had "Rocky" at the center).
Result: Turkers exhibited 62% accuracy with the navigation vectors and 37% accuracy with content vectors. We want to conduct several follow-up studies to understand different subject areas and parameter settings and user tasks, but the difference in performance was striking.
Our study shows the value of navigation vectors and makes me super excited to contribute to the engineering needed to get them out to the world on a regular basis. Imagine if every researcher and practitioner who uses word2vec now on Wikipedia content switches to navigation vectors. That's a huge audience!
Feedback and questions welcome!
-Shilad
Hi Shilad,
Thanks for this note.
This is super encouraging to see, given that a lot of my PhD work at Stanford has been on studying and leveraging users' Web (and in particular Wikipedia) navigation patterns, and on preaching the value hidden in navigation logs... ;)
We've found something similar for the task of link recommendation: here, too, recommendations mined from server logs outperform purely content-based methods: https://dlab.epfl.ch/people/west/pub/Paranjape-West-Leskovec-Zia_WSDM-16.pdf https://dlab.epfl.ch/people/west/pub/West-Paranjape-Leskovec_WWW-15.pdf
Curious to see how your line of research continues!
Bob
On Sun, Jun 18, 2017 at 3:51 PM, Shilad Sen ssen@macalester.edu wrote:
Hi Everybody,
I just ran an experiment that surprised me and I thought folks on this list would find interesting.
tl;dr We found that navigation vector embeddings for articles (as produced by Ellery Wulcyzn) outperform content-based vector embeddings (word2vec on article text) by 62% vs 37% accuracy in a task-based user study. I've volunteered to help with the engineering to productionize navigation embedding and this study reinforces my eagerness to get navigation vectors out in the world!
More detail: The maps we use in Cartograph (cartograph.info) are almost entirely built on "embedding" vectors for articles. We experimented with two word2vec-based embeddings: content vectors mined from article text and link structure, and navigation vectors mined from user browsing sessions. For the latter, we used Ellery Wulczyn's navigation vectors. By staring at maps, our intuition told us that the navigation vectors seemed better in "preference spaces" where the human taste space wasn't necessarily easily encoded into Wikipedia text.
Last weekend we ran a Mechanical Turk experiment to test this intuition. We created two Cartograph maps of movies: one built on navigation vectors and one built on content vectors. We identified 40 relatively popular movies that were not close neighbors in either map (i.e. cities that were not too close to each other) and ran a Mechanical Turk study using the maps.
For each Turker, we randomly selected 5 seen movies (out of the 30), and asked them to evaluate maps for each movie. For each movie city, we showed the map region around the city, but hid the city and asked them to guess the city from a list of 12 movies they had seen (screenshot below). We added in trivial validation questions using sequels to ensure Turkers were working in good faith (show a map for "Rocky II" that had "Rocky" at the center).
Result: Turkers exhibited 62% accuracy with the navigation vectors and 37% accuracy with content vectors. We want to conduct several follow-up studies to understand different subject areas and parameter settings and user tasks, but the difference in performance was striking.
Our study shows the value of navigation vectors and makes me super excited to contribute to the engineering needed to get them out to the world on a regular basis. Imagine if every researcher and practitioner who uses word2vec now on Wikipedia content switches to navigation vectors. That's a huge audience!
Feedback and questions welcome!
-Shilad
-- Shilad W. Sen
Associate Professor Mathematics, Statistics, and Computer Science Dept. Macalester College
Senior Research Fellow, Target Corporation
ssen@macalester.edu http://www.shilad.com https://www.linkedin.com/in/shilad 651-696-6273
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