Hierarchical recommender

Question

I have a hierarchical feature matrix, By that I mean that each item may belongs to one or more category, so my data will look something like that

|  User |  Categ  | Item  |
|  1       |   12      | 120   |
|  2       |   15      | 411   |
|  9       |   35      | 411   |
|  1       |   15      | 321   |

where the numbers represent ids for users, categories and items respectively.

As you can see there is a hierarchy here, a set of categories for each user and a set of items within that category, where one item can belong to more than one category.

My goal is to recommend new items to the new incoming users. I already have a user similarity algorithm in place and I can find the top n similar users. Now I need to use the above table to recommend one or more items to the new user.

• Easy way is to take the categories from the most similar user, pick some items from these categories and suggest them to the new user

But I am looking for a better approach that will consider the combination of categ-item simultaneously. This is a very large data set, so the algorithm should scale. Any suggestion?

Answer 1

This might be the core of a recommendation engine...so: 1. please see Machine Learning book from Manning ( a quick and dirty recommendation engine done in Python ) ...this is a nice blue print and starting point ...there is no silver bullet for recommendation engines recipes ( see Netflix case) 2. to scale up you will need to work with matrix decomposition and other linear algebra / algorithms (e.g. SVD ) or/and to work with parallel programming ( on power machines CUDA/OpenCL or in cluster e.g Hadoop)