نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مالی، دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، یزد، ایران.

2 گروه مالی و حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه یزد، یزد، ایران.

10.22105/imos.2021.290478.1117

چکیده

هدف: توسعه و پیشرفت علم و فن‌آوری، روزانه موجب ایجاد حجم زیادی از داده­های ساخت‌یافته، نیمه ساختاریافته و بدون ساختار با سرعتی بسیار سریع از منابع مختلف گردیده که منجر به اشتراک گذاشتن الگوهای مشترک بسیاری شده به‌نحوی‌که می­توان با استفاده از سیستم‌های توصیه کننده که بر اساس نیازهای کاربران طراحی شده با فیلتر و تجزیه‌وتحلیل این داده­هایشان، توصیه­هایی مربوط به محصول، کالا یا خدمات مورد علاقه آن‌ها ارائه داد. یکی از مسائل مهم در سیستم‌های توصیه کننده ارائه توصیه‌های شخصی‌سازی شده متناسب با روحیات کاربران است.
روش‌شناسی پژوهش: در این پژوهش با رویکرد نگاشت دانش با استفاده از کتابخانه bibliometrix در نرم‌افزار R به‌مرور کلیه پژوهش‌های انجام‌شده در خصوص کاربرد سیستم­های توصیه کننده در شخصی­سازی پرداخته می­شود.
یافته ها: در این پژوهش با استفاده از روش نگاشت دانش ضمن تعریف سیستم­های توصیه کننده و انواع آن، به معرفی نمای کلی از حیطه شخصی‌­سازی پرداخته و انواع مختلف شخصی­‌سازی ارائه می­‌شود. همچنین در مورد روند شخصی­‌سازی بحث نموده و در خصوص سیستم‌­های توصیه کننده به‌عنوان بخش جدایی‌ناپذیر از این فرایند نیز توضیحاتی مطرح شده است. در ادامه چالش­هایی که برای پیاده‌سازی سیستم‌­های توصیه کننده وجود دارد ارائه شده است و نهایتاً حوزه­هایی که بحث شخصی­سازی سیستم‌­های توصیه کننده می‌‌تواند در آن مطرح شود، ارائه می­شود.
اصالت/ارزش افزوده علمی: نتایج این پژوهش با مروری جامع بر کلیه پژوهش‌های این حوزه می‌تواند به‌عنوان جعبه‌ابزاری در جهت کاربرد سیستم‌­های توصیه کننده در شخصی‌­سازی محققان را در ایده پردازی و انتخاب روش مناسب در طبقه‌بندی و تحلیل داده‌ها یاری دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Typology of Personalization in Recommender Systems

نویسندگان [English]

  • Marziyeh Nourahmadi 1
  • Hojjatollah Sadeqi 2

1 Department of Financial Engineering, Faculty of Economic, Management and Accounting, Yazd University, Yazd, Iran.

2 Department of Finance and Accounting, Faculty of Management and Economics, Yazd University, Yazd, Iran.

چکیده [English]

Purpose: With the development of science and technology, large volumes of structured, semi-structured, and unstructured data are generated daily at breakneck speeds from various sources. This data generated by different users share many common patterns that can be filtered and analyzed to make recommendations for a product, goods, or service of interest to users. Recommender systems are software tools that provide suggestions to users based on their needs. One of the critical issues in recommender systems is providing personalized advice that fits the users' mood.
Methodology: In this research, with a bibliometric approach using the bibliometric library in R software, all the research done on applying recommended systems in personalization is reviewed.
Findings: Using the bibliometrics approach while defining recommender systems and their types, we presented an overview of the personalization field and different kinds of personalization. We also discussed the personalization process and described recommender systems as an integral part. Then, the challenges of implementing recommendation systems will be discussed, and finally, the areas in which the problem of personalization of recommendation systems can be raised.
Originality/Value: The present study, with a comprehensive review of all research in this field, can help researchers imagine and select appropriate methods for classifying and analyzing data as a toolbox for using recommender systems in personalization.

کلیدواژه‌ها [English]

  • Personalization
  • Recommender systems
  • User personality
  • Bibliometrics approach
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