See also here http://arxiv.org/abs/1006.2156
Another approach is to build a conventional recommender for items and attach
an indicator of how much information that recommender has to work with
(number of occurrences of the recommended item might be good enough). Then
do the same for some prominent characteristic of the items. This might give
you a "brand" recommender for retail products or an "artist" recommender for
music. For this more generic recommender, you might be able to directly
use the counts from the user's history. Finally, build "top-40" models for
overall item, brand, artist or what have you characteristics.
Now train a simple model to combine these results to find items that the
user is likely to engage with. SGD is an easy choice here. At
recommendation time, you would run all of the constituent recommenders and
use the SGD model to rescore the union of their results.
If done well, the brand and top-40 models will give you decent cold start
behavior while the real collaborative filtering models will give you good
performance after the cold-start. The SGD should be able to meld these
values well if it has a good indicator of how reliable each sub-model is.
On Mon, Feb 7, 2011 at 4:11 PM, Steven Bourke <[EMAIL PROTECTED]> wrote: