Comments (4)
recalculate_user
allows you to update user's latent factors if the confidence matrix has changed since the training. You can also use this to generate recommendations to users that were not in the training set. I mentioned this feature here: https://medium.com/@jbochi/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77
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Thanks Juarez, your blog is very useful!
So to summarise,
if you set recaluculate_user = True, it could be :
- the user not in the training set (new user)
- the user who is in the training set but consumes more items (new items)
In your blog, you set the confidence = 40, can i ask how you choose the value in this parameter?
Super Thanks,
Meiyi
from implicit.
Glad you find it useful, Meiyi. 40 was the value used in the original paper "Collaborative Filtering for Implicit Feedback Datasets" paper, but you should definitely do some kind of evaluation to pick the best value or weighting scheme for your dataset.
from implicit.
Thanks Juarez!!
from implicit.
Related Issues (20)
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