A new user similarity measure in a new prediction model for collaborative filtering

The Recommender Systems (RSs) based on the performance of Collaborative filtering (CF) depends on similarities among users or items obtained by a user-item rating matrix. The conventional measures such as the Pearson correlation coefficient (PCC), cosine (COS), and Jaccard (JACC) provide a varied and dissimilar value when the ratings between the users lie in the positive and negative side of the rating scale. These measures are also not very effective when there is sparsity in the rating matrix of the user-item. These problems are addressed by the Proximity-Impact-Popularity (PIP) similarity measure. Even though thePIPmethod provides an improved solution for this problem, the range of values for each component inPIPis very high. To address this issue and to improve the performance of a CF-based RS, a modified proximity-impact-popularity (MPIP) similarity measure is introduced. The expression is designed to getPIPvalues within the range of 0 to 1. A modified prediction expression is proposed to predict the available and unavailable ratings by combining user- and item-related components. The proposed method is tested by using various benchmark datasets. The size of the user-item sparse matrix varies to compare the performance of the methods in terms of mean absolute error, root mean squared error, precision, recall, and F-1-measure. The performance of the proposed method is statistically tested through the Friedman and McNemer test. The results obtained by using the evaluation criteria indicate that the proposed method provides a better solution than the conventional methods. The statistical analysis reveals that the proposed method provides minimumMAEandRMSEvalues. Similarly, it also provides a maximum F-1-measure for all the sub-problems.