Volume 16 - Issue 1
Filtering based on User Cluster for Collaboratively Incentivizing Users
Abstract
Giving or recommending appropriate content based on the quality of experience is the most important and challenging issue in recommender systems. 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 ratings given by users, which is thus easy to implement. We aim to design such a simple clustering based approach with no further prior information while improving the recommendation accuracy. To be precise, the purpose of CBCF with the IPU model is to improve recommendation performance such as precision, recall, andF1score by carefully exploiting different preferences among users. Specifically, we formulate a constrained optimization problem, in which we aim to maximize the recall(or equivalentlyF1score) for a given precision. To this end, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient. Afterwards, we give each item an incentive/penalty according to the preference tendency by users within the same cluster. Our experimental results show a significant performance improvement over the baseline CF scheme without clustering in terms ofrecallorF1score for a given precision.
Paper Details
PaperID: 201003
Author's Name: G. Bhanu Aparna, K. Praveen Kumar and R. Tamil Kodi
Volume: Volume 16
Issues: Issue 1
Keywords: Clustering, K-Means, Collaborative Filtering.
Year: 2020
Month: February
Pages: 13-18