Item Similarity Computation

One critical step in the item-based collaborative filtering algorithm is to compute the similarity between items and then to select the most similar items. The basic idea in similarity computation between two items i and j is to first isolate the users who have rated both of these items and then to apply a similarity computation technique to determine the similarity s_i,j. Figure 2 illustrates this process, here the matrix rows represent users and the columns represent items.

  

Figure 2: Isolation of the co-rated items and similarity computation

There are a number of different ways to compute the similarity between items. Here we present three such methods. These are cosine-based similarity, correlation-based similarity and adjusted-cosine similarity.