Affinity Propagation‐Based Hybrid Personalized Recommender System

Abstract

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation (AP), namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems.

A model for recommending suitable LSP for improved service delivery in mobile network using affinity propagation machine learning

“…It will be suitable for clustering the states and identifying similarities within them. The authors in [23] employed a nity propagation to develop a hybrid based recommender system. The authors in [24] successfully showed that a nity propagation model was a preferred model for clustering while the authors in [25] employed the a nity propagation model to identify similarities in image processing.…”