Mahout: Recommending Items for a user in particular product category

Question

What do we have as of now? - We are using Mahout's GenericItemBasedRecommender to get a list of recommended products for a user using TanimotoCoefficientSimilarity as ItemSimilarity.

Where do we want to go from here? - The above works fine when we don't care about product category but what we want to know is the Product Category specific recommendations i.e. Say if a user has been buying, browsing, liking etc. specifically more in Men's and Gadgets category, I would then want to show this user recommendation in that specific category saying Recommended for you in [X] where X would be replaced by Mens or Gadgets in this case. We are thinking about a couple of options below to achieve this and we need some leads/opinion/feedback etc. so as to make sure we are going in the right direction. Options:

1. Firstly we'll have to move to a non-tanimoto version for calculating item similarity so that we account for users buying, liking, etc and not only view/browsing data.

2. Figuring out product category for a particular user (this is where we need direction) - Our product category hierarchy is basically a tree and we need to know which top 4 nodes (with best recommendations) in tree we would show to the user. Also if we are saying that node X is a category which we are showing to the user and node Y is a parent of node X we then don't want show user products in category Y or any parent for that matter. Couple of ways achieving this:

   • For every user calculate SUM of similarity scores values of items for a nodes at leaf level and recursively calculate for parent node till the root. Now at each node we have A = SUM of similarity scores & B = Number of Items Recommended so we also have A/B=Value (V) at each node. Now we pick the top 4 V values from the tree and recommend that to the user. The challenge here is that if we try to calculate this online during the request it we would tough to limit this under 150 ms for the entire request. An Example:

      Root Level   -     Category12 (A=11, B=4) (category1 + category2)
                                             |
                        _____________________|_________________________
                      /                                                \
                     /                                                  \ 
     Leaf Level - category1 (A=6, B=2)                          category2 (A=5, B=2)
     

     Recommended products in Category 1: Item1 (score = 2), Item2 (score = 4)
     Recommended products in Category 2: Item3 (score = 1), Item4 (score = 4)

   • Second option: For every category create a cluster of users based on their behaviour (likes, buying, viewing etc.) and then figure out the top 4 categories to which the user belongs. Not sure if we can achieve this using clustering in Mahout but I think we can do this offline.

Please provide your feedback/suggestions/leads/thoughts.

Thanks in advance!

Answer 1

1. If you want to model more than one thing in your data, I would suggest to use the SVD recommender instead with the ALSWR factorizer set to implicit feedback. With that done you can have user,item,preference in your data and the preference value would be how strongly associated your user is to the item. You can play with the numbers, for example a purchase is a 20 and a view is just a 2. I'm just throwing numbers here, I wouldn't know what will work best for your data, because you can also model things proportionally, as in if a purchase is 30 times less likely to happen than a view, then a purchase should be 30 times stronger than a view.

2. Mahout provides a way to influence the recommendations through the IDRescorer. You implement your own logic here and decide how to affect the recommendations. For example, the IDRescorer would check if a recommendation candidate belongs to the same category and if it does, boost the score by X. There's an example here (link) from the Mahout in Action Book (which you should definitely read), showing a rescorer.

Hope this helps