Collaborative filtering recommender

This process executes a collaborative filtering recommender based on user to item score matrix. This recommender predicts one user’s score on some of his non scored items based on similarity with other users.

The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations and %{number_of_neighbors} defines the number of the most similar users taken into account while calculating the predicted score. The process internally uses a user to item matrix in a form of an example set where attributes of the matrix denote items; examples represent a single user while the attribute value denotes a user’s score on the item.

This process calculates Euclidean distance of the requested item to all items calculated only over the items for which the requested item has a score inputted. The resulting distance is recalculated into similarity and used as a weight while calculating weighted average of all the scores. The aggregated scores are then transposed to obtain the list of items and their scores and outputted as the final result.

The output of the process is an example set consisting of two attributes: recommendation and score of the recommendation.