Clustering-Based Collaborative Filtering Using an Incentivized Penalized User Model

Description

As collaborative Filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clustering – based CF (CBCF) method using an incentivized/penalized user (IPU) model only with the ratings given by users, which is thus easy to implement. CBCF with the IPU model is to improve recommendation performance such as precision, recall, and F1 score by carefully exploiting different preferences among users. users are divided into several clusters based on the actual rating data and Pearson correlation coefficient. Pearson’s correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. These efforts are mainly concentrated on the application of the shallow linear model, and relative works that deploy some deep learning components for item-based CF are scarce. an incentive/penalty according to the preference tendency by users within the same cluster. Our experimental results show a Signiant performance improvement over the baseline CF scheme without clustering in terms of recall or F1 score for a given precision.