تنوع‌بخشی شخصی‌سازی‌شده برای توصیه اقلام دنباله طولانی با استفاده از الگوریتم جستجوی ممنوعه چندهدفه

نویسندگان
الهه ملک‌زاده‌همدانی
مرجان کائدی
دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران
چکیده
در الگوریتم‌های توصیه‌ی رایج، تمرکز اصلی بر افزایش دقت توصیه‌ها است. این الگوریتم ها ممکن است قادر به یافتن تمامی اقلام مناسب برای توصیه به کاربران نباشند و تعداد زیادی از اقلام که توسط تعداد کمی از کاربران امتیازدهی شده‌اند، به ندرت در توصیه‌ها شرکت کنند. به این اقلام، اقلام دنباله طولانی گفته می‌شود. یکی از روش‌هایی که به مشارکت بیشتر اقلام دنباله طولانی کمک می‌کند توجه به ابعاد دیگری از سیستم توصیه‌گر مانند تنوع است. میزان گرایش کاربران به تنوع توصیه‌ها متفاوت است، بنابراین بهتر است تنوع‌بخشی متناسب با میزان تنوع‌گرایی کاربر انجام شود. در این پژوهش راه‌حلی مبتنی بر تنوع‌بخشی شخصی‌سازی‌شده برای مسئله عدم توصیه اقلام دنباله طولانی ارائه می شود. روش پیشنهادی یک روش بهینه‌سازی چندهدفه بر مبنای الگوریتم جستجوی ممنوعه است که در آن دو هدف برای توجه بیشتر به اقلام دنباله طولانی و یک هدف برای بهبود دقت لیست توصیه تعریف می‌شود. نتایج ارزیابی نشان می‌دهد که این روش در مقایسه با روش‌های قبلی کارایی بیشتری دارد و منجر به مشارکت بیشتر اقلام دنباله طولانی در توصیه ها می‌شود، ضمن اینکه دقت را در حد قابل قبولی حفظ می‌کند.

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علوم رایانش و فناوری اطلاعات
دوره 16، شماره 2
پاییز و زمستان
پاییز 1397