Very interesting article with some cool approaches. I'd be really interested to know how often the model needs to be trained? Seems like a lot of purchases are holiday/seasonally relevant and you'd hate to be suggesting valentine's day gifts on Feb 20th because everybody was buying them 2 weeks ago. Also be great to see any insights on how many features you need to get a good guess on a users tastes and preferences? Are 20 numbers enough to represent most of the dimensionality of Etsy products or 100 or 1000?
Comments
Very interesting article with some cool approaches. I'd be really interested to know how often the model needs to be trained? Seems like a lot of purchases are holiday/seasonally relevant and you'd hate to be suggesting valentine's day gifts on Feb 20th because everybody was buying them 2 weeks ago. Also be great to see any insights on how many features you need to get a good guess on a users tastes and preferences? Are 20 numbers enough to represent most of the dimensionality of Etsy products or 100 or 1000?
In their recent KDD article [1], Etsy used 200 SVD dimensions.
For those interested in trying this out in Python:
* `gensim` contains stochastic SVD for large data (fast online model training) [2]
* I wrote a benchmark of (approximate) nearest neighbour libraries in Python [3]
[1] https://dl.dropboxusercontent.com/u/2143857/papers/topics.pd...
[2] https://github.com/piskvorky/gensim/
[3] http://radimrehurek.com/2013/12/performance-shootout-of-near...