Chapter 2. Introducing recommenders
Table 2.1. An illustration of the average difference and root-mean-square calculation
Chapter 3. Representing recommender data
Table 3.1. Illustration of default table schema for taste_preferences in MySQL
Chapter 4. Making recommendations
Table 4.1. The Pearson correlation between user 1 and other users based on the three items that user 1 has in common with the others
Table 4.2. The Euclidean distance between user 1 and other users, and the resulting similarity scores
Table 4.3. The preference values transformed into ranks, and the resulting Spearman correlation between user 1 and each of the other users
Table 4.4. The similarity values between user 1 and other users, computed using the Tanimoto coefficient. Note that preference values themselves are omitted, because they aren’t used in the computation.
Table 4.5. The similarity values between user 1 and other users, computed using the log-likelihood similarity metric
Table 4.6. Evaluation results under various ItemSimilarity metrics
Table 4.7. Average differences in preference values between all pairs of items. Cells along the diagonal are 0.0. Cells in the bottom left are simply the negative of their counterparts across the diagonal, so these aren’t represented explicitly. Some diffs don’t exist, such as 102-107, because no user expressed a preference for both 102 and 107.