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Minaei2 B, Parvin H, Mirzarezaee M, Keshavarz A. A New WordNet Enriched Content-Collaborative Recommender System. JSDP. 2022; 18 (4) :89-124
URL: http://jsdp.rcisp.ac.ir/article-1-1185-fa.html
بحرانی پیام، مینایی بیدگلی بهروز، پروین حمید، میرزارضایی میترا، کشاورز احمد. ارائه یک سامانه پیشنهادگر حافظه پایه ترکیبی با استفاده از هستان‌شناسی و محتوا. پردازش علائم و داده‌ها. 1400; 18 (4) :124-89

URL: http://jsdp.rcisp.ac.ir/article-1-1185-fa.html


گروه مهندسی کامپیوتر، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی
چکیده:   (747 مشاهده)
سامانه­‌های پیشنهادگر در زمینه تجارت الکترونیک شناخته شده هستند. از این­‌گونه سیستم‌ها انتظار می‌­رود که کالاها و اقلام مهمی (از جمله موسیقی و فیلم) را به مشتریان پیشنهاد دهند. در سامانه‌های پیشنهادگر سنتی از جمله روش­های پالایش محتوا پایه و پالایش مشارکتی، چالش‌­ها و مشکلات مهمی از جمله شروع سرد، مقیاس‌­پذیری و پراکندگی داده‌­ها وجود دارد. اخیراً به‌­کارگیری روش‌­های ترکیبی توانسته با بهره‌­گیری از مزایای این روش‌­ها با هم، برخی از این چالش‌­ها را تا حد قابل قبولی حل نمایند. در این مقاله سعی می­‌شود روشی برای پیشنهاد ارائه شود که ترکیبی از دو روش پالایش محتوا پایه و پالایش مشارکتی (شامل دو رویکرد حافظه پایه و مدل پایه) باشد. روش پالایش مشارکتی حافظه پایه، دقت بالایی دارد، اما از مقیاس‌­پذیری کمی برخوردار است. در مقابل، رویکرد مدل پایه دارای دقت کمی در ارائه پیشنهاد به کاربران بوده اما مقیاس‌­پذیری بالایی از خود نشان می­‌دهد. در این مقاله سامانه پیشنهادگر ترکیبی مبتنی بر هستان­‌شناسی ارائه شده که از مزایای هر دو روش بهره برده و  براساس رتبه‌­بندی­‌های واقعی، مورد ارزیابی قرار می­‌گیرد. هستان‌شناسی، توصیفی واضح و رسمی برای تعریف یک پایگاه دانش شامل مفاهیم (کلاس‌­ها) در حوزه موضوعی، نقش‌­ها (رابط­‌ها) بین نمونه‌­های مفاهیم، محدودیت­‌های مربوط به رابطه‌­ها، همراه با یک مجموعه از عناصر و اعضا (یا نمونه­‌ها) است که یک پایگاه دانش را تعریف می­‌کند. هستان­‌شناسی در بخش پالایش محتوا پایه مورد استفاده قرار می­‌گیرد و ساختار هستان‌شناسی توسط تکنیک­‌های پالایش مشارکتی بهبود می­‌یابد. در روش ارائه‌شده در این پژوهش، عملکرد سیستم پیشنهادی بهتر از عملکرد پالایش محتوا پایه و مشارکتی است. روش پیشنهادی با استفاده از یک مجموعه‌داده­ واقعی ارزیابی شده است و نتایج آزمایش­ها نشان می‌دهد روش مذکور کارایی بهتری دارد. همچنین با توجه به راه‌کارهای ارائه‌شده در مقاله حاضر، مشخص شد، روش پیشنهادی دقت و مقیاس‌­پذیری مناسبی نسبت به سامانه‌های پیشنهادگری دارد که صرفاً حافظه پایه (KNN) و یا مدل پایه هستند.
شماره‌ی مقاله: 7
متن کامل [PDF 3065 kb]   (168 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش داده‌های رقمی
دریافت: 1399/8/4 | پذیرش: 1399/12/18 | انتشار: 1401/1/1 | انتشار الکترونیک: 1401/1/1

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