A Bayesian Inference Based Hybrid Recommender System
| Autor: | Walid El Ayeb, Zied Choukair, Armielle Noulapeu Ngaffo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Bayesian inference Dirichlet distribution Recommendation quality 02 engineering and technology Recommender system Machine learning computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering General Materials Science Selection (genetic algorithm) recommender system business.industry General Engineering collaborative filtering maximum a posteriori estimation 020201 artificial intelligence & image processing The Internet Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Hybrid model computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 101682-101701 (2020) |
| ISSN: | 2169-3536 |
| Popis: | The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of model-based methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality. |
| Databáze: | OpenAIRE |
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