User-based collaborative filtering recommender system template

The workflow for user-based collaborative filtering, takes only one example set as an input: a user-item matrix, where the attributes denote item IDs, and rows denote users. If a user i has rated an item j with a score s, the matrix will have the value s written in i-th row and j-th column. In the context of the process we define the ID of the user %{id}, desired number of recommendations %{recommendation_no}, and the number of neighbors used in calculating user to user distance %{number_of_neighbors}.

The first step of the workflow is to set all the zero values in the matrix to missing values in order to ignore them later in the aggregation process. Afterwards, we separate our user of interest from all the users in the example set and then calculate the similarities between our user and the %{number_of_neighbors} nearest users. We join the resulting similarities as weights with the example set of all users without our user of interest. As a result of this join we obtain a list of the most similar %{number_of_neighbors} users and their ratings with a weight that equals their similarity to our user of interest. Afterwards, we aggregate the ratings using these  similarities as weights and select only the items our user did not rate and for which we could calculate the rating. The final result is a list of items and their scores trimmed to %{recommendation_no} recommendations.

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