Good idea Gokhan, thanks!
@ Sigbjørn: Thanks for the feedback. In fact we have plenty of [implicit]
preference data for each user, e.g. product view. What I found out is that
data from a certain point in time were very noisy and inconsistent, when I
started fetching from that point on I got much better results.
On Wed, Nov 6, 2013 at 2:24 PM, Gokhan Capan <gkhn...@gmail.com> wrote:
> 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
> >
>
