A Hybrid Recommender System Using KNN and Clustering

Recommender Systems (RSs) are known in the E-Commerce (EC) field. They are expected to suggest the accurate goods/musics/films/items to the consumers/clients/people/users. Recent Hybrid RSs (HRSs) have made us able to deal with the most important shortages of traditional Content-based F iltering (ConF) and Collaborative Filtering (ColF). Cold start, scalability and sparsity are the most important challenges to EC recommender systems (ECRS). HRSs combine ConF and ColF. While the RSs 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, HRS 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 ConF segment of the proposed HRS. 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 ConF and ColF 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.