I think that Dominik's comments are exactly on target. As far as implementation is concerned, I think that it is very important to not distort the basic recommendation algorithm with business rules like this. It is much better to post-process the results to impose your will directly. One exception to this is that I think it is reasonable to use ordered cooccurrence and also repeated cooccurrence here for some hints here. This lets you determine likely accessories (purchased after the main item, mostly) and also find razor-blades (highly repetitive purchases). You still have the problem of flooding with similar items.
The diversity that you are talking about is a critical quality in recommendation results. The basic intuition is that recommendation results are not individual recommendations, but are included in a portfolio of recommendations. You need the diversity in this portfolio because if you are wrong about an item, the likelihood of being wrong about very similar items is high. If you flood the first and second pages with these similar items, then you don't have room for the alternative items that might well be correct. My approach in the past was to define heuristic definitions for "too similar" and do a pass over the sorted recommendation results giving each item that passes the too-similar criterion a penalty score. When done with this, I re-sort the results and the duplicative content falls to the bottom of the recommendations. On Thu, Sep 5, 2013 at 1:15 AM, Dominik Hübner <cont...@dhuebner.com> wrote: > Just a quick a assumption, maybe I have not thought this through enough: > > 1. Users probably tend to compare products => similar VIEWS > 2. User as well might tend to PURCHASE accessory products, like the laptop > bag you mentioned > > May be you could filter out products that have a similarity computed from > the product views, but leave those similar, based on purchases, in your > recommendation set? > > Nevertheless, I guess this will be strongly depending on the domain the > data comes from. > > > On Sep 5, 2013, at 10:07 AM, Nick Pentreath <nick.pentre...@gmail.com> > wrote: > > > Hi all > > > > Say I have a set of ecommerce data (views, purchases etc). I've built my > > model using implicit feedback ALS. Now, I want to add a little bit of > > "smart filtering". > > > > Filtering based on not recommending something that has been purchased is > > straightforward, but I'd like to also filter so as not to recommend > "highly > > similar" items to someone who has purchased an item. > > > > In other words, if someone has just purchased a laptop, then I'd like to > > not recommend other laptops. Ideally while still recommending "related" > > items such as laptop bags, mouse etc etc. (this is just an example). > > > > Now, I could filter based on metadata tags like "category", but assuming > I > > don't always have that data, then simplistically I have the option of > > filtering out products based on those that have high cosine similarity to > > the purchased products. However, this risks filtering out "good" similar > > products (like the laptop bags) as well as the "bad" similar products. > > > > I'm experimenting with building a second variant of the model that > > effectively downweights "views" to near zero, hence leaving something > sort > > of like a "purchased together" model variant. Then recommendations can be > > made using this model when a user purchases an item (or perhaps a > re-scorer > > that is a weighted variant of model A and model B but that tends to > weight > > model B - the purchased together model - higher) > > > > Are there other mechanisms to tweak the ALS model such that it tends > > towards recommending "related products" (but not "highly similar of the > > exact same narrow product type")? > > > > Any other ideas about how best to go about this? > > > > Many thanks > > Nick > >
