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From Dominik Hübner <cont...@dhuebner.com>
Subject Re: Tweaking ALS models to filter out "highly related" items when an item has been purchased
Date Thu, 05 Sep 2013 08:15:26 GMT
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.pentreath@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


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