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Recommender systems using cloud-based computer networks to predict service quality | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 28، دوره 15، شماره 10، دی 2024، صفحه 359-376 اصل مقاله (628.34 K) | ||
| نوع مقاله: Review articles | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31196.4588 | ||
| نویسندگان | ||
| Mehran Aghaei1؛ Sepideh Adabi* 1؛ Parvaneh Asghari2؛ Hamid Haj Seyyed Javadi3 | ||
| 1Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 3Department of Mathematics and Computer Science, Shahed University, Tehran, Iran | ||
| تاریخ دریافت: 19 اردیبهشت 1402، تاریخ بازنگری: 29 مرداد 1402، تاریخ پذیرش: 05 شهریور 1402 | ||
| چکیده | ||
| In recommender systems, the user items are offered tailored to users’ requirements. Because there are multiple cloud services, recommending a suitable service for users' requirements is of paramount importance. Cloud recommender systems are qualified depending on the extent to which they accurately predict service quality values. Because no service was chosen by the user beforehand, and no record of the user's selections is available, it became challenging to recommend it to users. To promote the recommender system quality, to accurately predict service quality values by offering various procedures, including collaborative filtering, matrix factorization, and clustering. This review article first mentions the general problem and states the need for research, followed by examining and expressing the kinds of recommender systems along with their problems and challenges. In the present review, various approaches, platforms, and solutions are reviewed to articulate the pros and cons of individual approaches, simulation models, and evaluation metrics employed in the reviewed techniques. The measured values in various approaches of the papers are compared with one another in several diagrams. This review paper reviews and introduces the entire datasets applied in the studies. | ||
| کلیدواژه‌ها | ||
| QoS prediction؛ Recommendation system؛ collaborative filtering؛ Matrix factorization؛ web services in the cloud | ||
| مراجع | ||
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