Cassio,
I would implement a CandidateItemsStrategy that returns products that are
available now. A neighborhood based recommender would iterate over those
products, and rank them based on the similarity measure you provide.
If the DataModel of your recommender does not contain most of your
available items, you may want to overcome this "cold-start" challenge by
making your similarity take content features into account.
Hope that helps,
Best
Gokhan
On Wed, Nov 6, 2013 at 1:40 PM, Sigbjørn Dybdahl <sigbj...@fifty-five.com>wrote:
> Hi,
>
> Could this be due to a small number of items viewed/liked/purchased per
> user?
>
> Correct me if I'm wrong, but this would make the total recommendation space
> sparse, thus making it hard to find good recommendations (ie
> recommendations which are relevent and not obsolete). If so, it might be
> worth considering simpler approaches than CF.
>
>
> Sigbjørn
>
>
> On 22 October 2013 12:43, Cassio Melo <melo.cas...@gmail.com> wrote:
>
> > Hi all,
> >
> > I have a product recommendation use case for an e-commerce site and I've
> > been playing around mahout's CF capabilities lately. I reached a point
> > where I should ask a feedback from the community on my approach. I'm
> > struggling to get ANY recommendation.
> >
> > Here's what I've done so far:
> >
> > 1) Collected data in this format:
> >
> > USER_ID,PRODUCT_ID,VIEW,LIKE,FAVORITE,PURCHASE
> > 0001,3333,1,0,1,1
> >
> > 2) Built preferences for each user based on a computed score [0,1] for
> the
> > attributes (view,like,favorite,purchase)
> >
> > 3) Implemented a custom ItemSimilarity class to boost products in the
> same
> > type, from the same manufacturer, with similar prices, etc
> >
> > 4) Implemented a custom IDRescorer to filter out products that are no
> > longer available.
> >
> > 5) Implemented a Recommender subclass that simply instantiates a
> > GenericItemRecommender with my custom similarity class: recommender = new
> > GenericItemBasedRecommender(myModel, new ProductSimilarity());
> >
> > It happens that <b>most</b> of product/preferences data is historical and
> > most products are no longer available. Because of this I get zero
> > recommendations when the IDRescorer's filter is activated (yes I checked
> > the isFiltered method, it is returning true for expired products and
> false
> > otherwise as expected) event though there are lots of valid products.
> >
> > Here's the overridden method in the IDRescorer's class:
> >
> > public boolean isFiltered(long id) {
> >
> > Product a = PreferencesDataModel.lookupProduct(id);
> >
> > return ! a.isActive(); // filter expired product
> >
> > }
> >
> > I see it as beneficial to feed the engine with historical data on expired
> > products and filtering them for recommendation, but getting zero
> > recommendations made me rethink this approach (I also tried different
> > similarity metrics including UserSim). What do you guys think?
> >
>
>
>
> --
> Sigbjørn DYBDAHL | 55 | fifty-five.com <http://www.fifty-five.com/> | 4,
> place de l'Opéra, 75002 Paris | 01 76 21 91 32
>
