Basic user recommendation is now available on the “Users” tab.
Recommendation algorithms available:
- By tag frequency for photo contributions of each user: what the system does is score each other user on the database based on the number of tags you have in common. Each user is represented internally as a sparse vector with tags as dimensions, where the value on each of these dimensions equal the number of Flickr contributed photos that contain that tag. A vector product is then made with each other user in order to find the most similar one. Several different metrics may be used to tell how similar each user is.
- More to come !
We have around 1.8mi user-tag tuples on the database so a recommendation takes about 6 seconds, which is really not good since server CPU usage stays at 100% during this time. This is going to improve soon as I work around better indexing and scoring techniques.
Completely hidden from users is an A/B test for determining recommendation algorithm variants that work better. Feedback provided by users are stored together with parameters chosen randomly for each generated recommendation.
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