پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 663 |
| تعداد مقالات | 9,680 |
| تعداد مشاهده مقاله | 68,887,886 |
| تعداد دریافت فایل اصل مقاله | 48,422,228 |
Landscape view of recommender system techniques based on sentiment analysis | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 117، دوره 14، شماره 1، فروردین 2023، صفحه 1539-1546 اصل مقاله (437.19 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.7138 | ||
| نویسندگان | ||
| Rosul Ibrahim Kazem1؛ Enas Fadhil Abdullah* 2 | ||
| 1Department of Computer Science, Collage of Education, University of Kufa, Najaf, Iraq | ||
| 2Department of Computer Science, Collage of Education for Girls, University of Kufa, Najaf, Iraq | ||
| تاریخ دریافت: 21 تیر 1401، تاریخ بازنگری: 29 مرداد 1401، تاریخ پذیرش: 21 مهر 1401 | ||
| چکیده | ||
| Over the last several years, sentiment analysis has emerged as one of the most popular applications of machine learning. It enables the identification of a user's attitude from a remark, document, or review. As a result of the development of Big Data, recommender systems (RS) are also finding more use in many aspects of day-to-day living. There are three basic kinds of RS: collaborative filtering, content-based, and hybrid. This article presents a quick description of the recommender systems supplemented with a sentiment analysis module. Sentiment Analysis systems may help recommender systems improve by assessing Web-based reviews. | ||
| کلیدواژه‌ها | ||
| recommender systems؛ contextual meaning؛ sentiment analysis | ||
|
آمار تعداد مشاهده مقاله: 17,315 تعداد دریافت فایل اصل مقاله: 9,284 |
||