The cluster was mostly intended for tackling the cold start problem for new users. I want to build a recommender based on existing components or to be precise a combination of them.
Unfortunately, the only product meta-data I currently have is the product price. Furthermore, this is a project I am working on alone. As a consequence, the approaches I can examine in the given time are limited. Would using ALS and ranking its outcome by e.g. frequent item set algorithms be something worth looking into? Or did you mean something different? My personal goal is to build a recommender providing acceptable results using the data I currently have available. Of course, this will only serve as a basis for further improvements where necessary or if further information can be obtained. On May 6, 2013, at 11:21 PM, Ted Dunning <[email protected]> wrote: > Are you looking to build a product recommender based on your own design? > Or do you want to build one based on existing methods? > > If you want to use existing methods, clustering has essentially no role. > > I think that composite approaches that use item meta-data and different > kinds of behavioral cues are important to best performance. > > > On Mon, May 6, 2013 at 12:35 PM, Dominik Hübner <[email protected]>wrote: > >> Well, as you already might have guessed, I am building a product >> recommender system for my thesis. >> >> I am planning to evaluate ALS (both, implicit and explicit) as well as >> item -similarity recommendation for users with at least a few known >> products. Nevertheless, the majority of users only has seen a single (or >> 2-3) product(s). I want to recommend them the most popular items from >> clusters, their only product comes from (as a workaround for the cold-start >> problem). Furthermore, I expect to be able to see which "kind" of products >> users like. This might provide me some information about how well ALS and >> similarity recommenders fit the user's area of interest (an early >> evaluation) or at least to estimate if the chosen approach will work in >> some way. >> >> On May 6, 2013, at 9:09 PM, Ted Dunning <[email protected]> wrote: >> >>> I don't even think that clustering is all that necessary. >>> >>> The reduced cooccurrence matrix will give you items related to each item. >>> >>> You can use something like PCA, but SVD is just as good here due to near >>> zero mean. You could SSVD or ALS from Mahout to do this analysis and >> then >>> use k-means on the right singular vectors (aka item representation). >>> >>> What is the high level goal that you are trying to solve with this >>> clustering? >>> >>> >>> >>> >>> On Mon, May 6, 2013 at 12:01 PM, Dominik Hübner <[email protected] >>> wrote: >>> >>>> And running the clustering on the cooccurrence matrix or doing PCA by >>>> removing eigenvalues/vectors? >>>> >>>> On May 6, 2013, at 8:52 PM, Ted Dunning <[email protected]> wrote: >>>> >>>>> On Mon, May 6, 2013 at 11:29 AM, Dominik Hübner <[email protected] >>>>> wrote: >>>>> >>>>>> Oh, and I forgot how the views and sales are used to build product >>>>>> vectors. As of now, I implemented binary vectors, vectors counting the >>>>>> number of views and sales (e.g 1view=1count, 1sale=10counts) and >>>> ordinary >>>>>> vectors ( view => 1, sale=>5). >>>>>> >>>>> >>>>> I would recommend just putting the view and sale in different columns >> and >>>>> doing cooccurrence analysis on this. >>>> >>>> >> >>
