A hybrid semantic recommender system enriched with an imputation method

• Published in: Multimedia tools and applications 2024-02, Vol.83 (6), p.15985-16018
• Main Authors: Bahrani, Payam, Minaei-Bidgoli, Behrouz, Parvin, Hamid, Mirzarezaee, Mitra, Keshavarz, Ahmad
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Collaboration; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Filtering systems; Filtration; Hybrid systems; Model-based systems; Multimedia Information Systems; Recommender systems; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-15258-4

Recommender systems are widely used in many applications. They can be viewed as the predictor systems that are to suggest accurate and highly preferred items to consumers or clients. These systems can be considered to be information filtering systems. They counter some important challenges such as cold start (it means the absence of enough data for a new item to make accurate recommendations), scalability, and sparsity. The memory-based recommender systems have high accuracy but lack scalability. Also, the model-based systems are scalable but not accurate. Current recommender systems use hybrid methods to deal with the most important shortages of traditional filtering approaches. Current recommender systems are usually a hybrid of content-based filtering and collaborative filtering, and so on. In this paper, a hybrid recommender system is presented to meet the stated challenges, increase system performance and provide more accurate recommendations. This system uses both content-based filtering and collaborative filtering. In addition, using an automatically collected wordnet, we create an ontology that has been used in the content-based filtering section of our proposed approach. Furthermore, this framework applies KNN ( k nearest neighbors ) algorithm and clustering to improve its functionality. The proposed system is evaluated on a real benchmark. The experimentations show the proposed method has a better performance compared with the current superior related methods. The experimentations also show that our recommender system has desirable scalability compared with the state-of-the-art recommender systems.