Item-based collaborative filtering recommender system template

The workflow for item-based collaborative filtering receives a user-item matrix for its input, and the same context defined macros as the user-based recommender template, namely %{id}, %{recommendation_no}, and %{number_of_neighbors}.

Although this process is in theory very similar to user-based technique, it differs in several processing steps since we are dealing with an item-user matrix, the transposed user-item example set. The first step of the workflow, after declaring zero values missing, is transposing the user-item matrix and calculating the item to %{number_of_neighbors} nearest items similarities. The resulting example set consists of IDs of items, IDs of their most similar items and similarity values. This is joined with IDs of items which our user of interest rated. As a result we obtain an example set of item IDs, their scores, and similarities to the items our user has rated. With the next join we filter only those items that our user had no contact with. The last step is to aggregate that data; group each item ID and calculate the average of that item's rating weighted by the similarity of that item to items our user of interest did rate. The operators following this aggregation, grouped in "Postprocessing" subprocess, only format the output and trim the number of examples in the example set to %{recommendation_no}.

Reference:

Bošnjak, M., Antulov-Fantulin, N. Šmuc, T. and Gamberger, D., Constructing recommender systems workflow templates in RapidMiner, Proceedings of the RapidMiner Community Meeting And Conference (RCOMM), 2011, Dublin, Ireland - In Press