List of tables 2. 1 Comparison between USER-BASED kNN and ITEM-BASED knn CF algorithms. 19 2.2 Properties of the datasets 3.1 Influence measures compared against influence principles 4.1 Relationship between ENIPD and #of ratings of the users 4.2 Squared correlation coefficient between the actual values and predicted values of EniD 4.3 Weights of the features near SvM modeling 6.1 Showing a limitation of entropy 6.2 ENTROPYO computation of the item a, which has been voted 200 times and the votes are uniformly distributed across the rating rating-scale of (1-5) 6. 3 ENTROPYO computation of the item b, which has been voted 3, 000 times and the votes are unanimously 5 6.4 Effect of applying a log transformation to items'rating frequency 6.5 Average percentage of overlapped items between the pairs of item-infuence measures 6.6 Properties of the user-specific top 20 items by various item-influence measures 110 7.1 Table of notations 7. 2 Showing the strengths and weaknesses of the item influence approaches in 127 7. 3 Group-wise participations of the subjects 130 7.4 Effectiveness of the learned user profiles according to th racy of the initial recommendations on two CF algorithms 8. 1 Contrasts testing the four hypotheses Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission