Hmm. Cool thanks, looks like I got to do a lot of reading.
On Fri, Oct 1, 2010 at 12:07 AM, Sean Owen wrote:
> Sure. I would suggest you create an ItemSimilarity implementation which
> loads this additional information, and constructs some formula for
> similarity based on whether movies share g
Sure. I would suggest you create an ItemSimilarity implementation which
loads this additional information, and constructs some formula for
similarity based on whether movies share genre, actors, etc. For example
maybe being in the same genre is worth +0.1 similarity. Maybe same actor is
worth +0.2.
And if you want to see more about recommendation using side data as well as
interaction data,
the best reference I know of is Menon and Elkan's recent paper:
http://arxiv.org/abs/1006.2156
On Thu, Sep 30, 2010 at 4:45 PM, Sebastian Schelter wrote:
> If you just wanna know more about the theory
Hi Mac,
Collaborative Filtering algorithms only learn from interaction data
(known preferences) and are content agnostic, which means they don't
look at the actual content of the items.
This might sound awkward and counterintuitive at a first look but it
works really well when applied.
The relat
I have got the group lens example working. Had a couple of doubts though -
The dataset in grouplens has movieid, userid and the corresponding ratings.
However a rating is meant to rate a movie but there are other things related
to a movie to which the rating contributes.
For example, the actors, di
