یک بررسی سیستماتیک در مورد استفاده از الگوریتم های یادگیری ماشین
ترجمه نشده

یک بررسی سیستماتیک در مورد استفاده از الگوریتم های یادگیری ماشین

عنوان فارسی مقاله: استفاده از الگوریتم های یادگیری ماشین در سامانه های توصیه گر: یک بررسی سیستماتیک
عنوان انگلیسی مقاله: The use of machine learning algorithms in recommender systems: A systematic review
مجله/کنفرانس: سیستم های خبره با برنامه های کاربردی - Expert Systems with Applications
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، هوش مصنوعی، مهندسی نرم افزار
کلمات کلیدی فارسی: بررسی سیستماتیک ادبیات، سامانه های توصیه گر، یادگیری ماشین، الگوریتم های یادگیری ماشین، دامنه های کاربردی، معیارهای عملکرد
کلمات کلیدی انگلیسی: Systematic review of the literature، Recommender systems، Machine learning، Machine learning algorithms، Application domains، Performance metrics
نوع نگارش مقاله: مقاله مروری (Review Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2017.12.020
دانشگاه: David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West Waterloo, N2L 3G1, Canada
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 5/891 در سال 2018
شاخص H_index: 162 در سال 2019
شاخص SJR: 1/190 در سال 2018
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 31
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11368
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Theoretical background


3- Systematic review


4- Systematic review results


5- Conclusions and future work


References

نمونه متن انگلیسی مقاله

Abstract


Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.


Introduction


Recommender systems (RSs) are used to help users find new items or services, such as books, music, transportation or even people, based on information about the user, or the recommended item (Adomavicius & Tuzhilin, 2005). These systems also play an important role in decision-making, helping users to maximize profits (L.-S. Chen, Hsu, Chen, & Hsu, 2008) or minimize risks (Bouneffouf, Bouzeghoub, & Gancarski, 2013). Today, RSs are used in many information-based companies such as Google (J. Liu, Dolan, & Pedersen, 2010), Twitter (Ahmed et al., 2013), LinkedIn (Rodriguez, Posse, & Zhang, 2012), and Netflix (Steck, 2013). The field of RS has its origins in the mid-1990s with the introduction of Tapestry (Goldberg, Nichols, Oki, & Terry, 1992), the first RS. As the RS field evolved, researchers studied the use of algorithms from machine learning (ML), an area of artificial intelligence (AI). ML has been studied since the late 1950s (Martens, 1959), with the emergence of the field of AI. Today, there is a plethora of ML algorithms (k-nearest neighbor (Patrick & Fischer III, 1970), clustering (Jain, Murty, & Flynn, 1999), Bayes network (Friedman, Geiger, & Goldszmidt, 1997), to name a few types), which are used in applications that range from vacuum cleaner robots (Burhans & Kandefer, 2004) and assistance for disabled people (Karimanzira, Otto, & Wernstedt, 2006) to pattern recognition in images (Torralba, Fergus, & Weiss, 2008), or self-driving vehicles (Thrun, 2007). The potential application of ML algorithms is vast and the field looks very promising. ML algorithms are being used in RSs to provide users with better recommendations. However, the ML field does not have a clear classification scheme for its algorithms, mainly because of the number of approaches and the variations proposed in the literature (Lv & Tang, 2011). As a consequence, it becomes difficult and confusing to choose an ML algorithm that fits one’s need when developing an RS. In addition, researchers may find it challenging to track the use and the trends of ML algorithms in RSs. One way to assist researchers and practitioners (e.g. software engineers and developers (Pressman, 2015; Isazadeh, 2004)) in choosing which ML algorithm to use in an RS is the study of the RS and ML fields. Research about RSs containing ML algorithms implemented in the literature can help show trends and provide a direction for future studies.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

یک بررسی سیستماتیک در مورد استفاده از الگوریتم های یادگیری ماشین
نوشته های مرتبط
مقالات جدید
لوگوی رسانه های برخط

logo-samandehi

پیوندها