A Hybrid Recommender System Using KNN and Clustering

Abstract

Recommender Systems ([Formula: see text]) are known in the E-Commerce ([Formula: see text]) field. They are expected to suggest the accurate goods/musics/films/items to the consumers/clients/people/users. Recent Hybrid [Formula: see text]s ([Formula: see text] have made us able to deal with the most important shortages of traditional Content-based F iltering ([Formula: see text]) and Collaborative Filtering ([Formula: see text]). Cold start, scalability and sparsity are the most important challenges to [Formula: see text] recommender systems ([Formula: see text]). [Formula: see text]s combine [Formula: see text] and [Formula: see text]. While the [Formula: see text]s that are based on memory have high accuracy, they are not scalable. Contrarily, the RSs on the basis of models have low accuracy but high scalability. Thus, aiming at dealing with cold start, scalability and sparsity challenges, [Formula: see text] is proposed to use both methods and also it has been evaluated on a real benchmark. An ontology, which is automatically created by an intelligently collected wordnet, has been employed in [Formula: see text] segment of the proposed [Formula: see text]. It has been automatically created and enhanced by an additional process. The functionality of the recommended framework has been superior to the performance of the state-of-the-art methods and the traditional [Formula: see text] and [Formula: see text] embedded in our method. Using a real dataset as a benchmark, the experimentations indicate that the proposed method not only has better performance but also has more efficacy rather than the state-of-the-art methods.