Content-based recommending with Taste

[Marko Ciric <ci...@gmail.com>]

Hi guys,

I'm currently using a generic item-based recommender as a content-based
recommender by customizing the item similarity to compare item features.
Basically, I want to solve a cold-start problem I have with my small data
set. I'm wondering now, is there a way I can use Taste existing recommender
evaluators to evaluate my content-based recommender. Any hints?

-- 
--
Marko Ćirić
ciric.marko@gmail.com

Re: Content-based recommending with Taste

[Sean Owen <sr...@gmail.com>]

You should be able to use RecommenderEvaluator (well, its subclasses) just
the same way as with any other Recommender. It is no different just because
you have a custom ItemSImilarity.

On Fri, Feb 25, 2011 at 6:47 PM, Marko Ciric <ci...@gmail.com> wrote:

> Hi guys,
>
> I'm currently using a generic item-based recommender as a content-based
> recommender by customizing the item similarity to compare item features.
> Basically, I want to solve a cold-start problem I have with my small data
> set. I'm wondering now, is there a way I can use Taste existing recommender
> evaluators to evaluate my content-based recommender. Any hints?
>
> --
> --
> Marko Ćirić
> ciric.marko@gmail.com
>

Re: Content-based recommending with Taste

[Ted Dunning <te...@gmail.com>]

One approach is to recommend content items as well as items and fold these
recommendations together.  Content-based recommendations are then done by
retrieving items with weighted combinations of the recommended content
values.  Retrieval of items from content should be roughly in descending
popularity order.

One way to fold the results is simply to take the content recommendations in
cold start situations, the item recommendations for hot items and the union
for intermediate situations.  A slightly more principled way to combine is
to compute a score for each item in the union of content and item-based
recommendations using something like an SGD model.  The inputs to the model
are the content score (if any), the item score (if any) and a variety of
features that help the model decide how to transition between the two
recommendation models.

Another approach is to recommend from a combined set of items and content
values.  To generate the final result, dereference the content values, but
pass the items through verbatim.  Since you usually need a retrieval step to
decorate the items with additional data, this dereference of content and
item id's is often free.

On Fri, Feb 25, 2011 at 10:47 AM, Marko Ciric <ci...@gmail.com> wrote:

> Hi guys,
>
> I'm currently using a generic item-based recommender as a content-based
> recommender by customizing the item similarity to compare item features.
> Basically, I want to solve a cold-start problem I have with my small data
> set. I'm wondering now, is there a way I can use Taste existing recommender
> evaluators to evaluate my content-based recommender. Any hints?
>
> --
> --
> Marko Ćirić
> ciric.marko@gmail.com
>