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Intelligent Online Store: User Behavior Analysis based Recommender System | ||
Journal of Information Technology Management | ||
مقاله 10، دوره 7، شماره 2، مهر 2015، صفحه 385-406 اصل مقاله (570.97 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jitm.2015.53884 | ||
نویسندگان | ||
Mohamadreza Karimi Alavije1؛ Shiva Askari* 2؛ Sirvan Parasteh3 | ||
1Assistant Prof., Business Management, Faculty of Management and Accounting University of Allameh Tabatabayi, Tehran, Iran. | ||
2MSc Student, Business Management, Faculty of Management and Accounting, Allameh Tabatabayi University, Tehran, Iran. | ||
3MSc, Artifiacial Intelligent, Faculty of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. | ||
چکیده | ||
Recommender systems provide personalised recommendations to users, helping them find their ideal items, also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful techniques utilized in these systems facilitating the provision of recommendations close to that of the customer's taste and need. However the proliferation of both customers and products on offer, the technique faces some issues such as "cold start" and scalability. As such in this paper a new method has been introduced in which user-based collaborative filtering is used at a base method along with a weighted clustering of users based upon demographics in order to improve the results obtained from the system. The implementation of the results of the algorithms demonstrate that the presented approach has a lower RMSE, which means that the system offers improved performance and accuracy and that the resulting recommendations are closer to the taste and preferences of the users. | ||
کلیدواژهها | ||
Clustering؛ Collaborative filtering؛ demographics؛ Recommender System | ||
عنوان مقاله [English] | ||
فروشگاه اینترنتی هوشمند: سیستم پیشنهاددهندۀ مبتنی بر تحلیل رفتار کاربران | ||
چکیده [English] | ||
سیستمهای پیشنهاددهندهای که با ارائۀ پیشنهادها شخصیسازی میشوند و به کاربران در یافتن محصولی که علاقه دارند، کمک میکنند، میتوانند در ترغیب مشتریان به خرید از وبسایت و در نتیجه موفقیت فروشگاههای آنلاین، نقش کلیدی ایفا کنند. روش پالایش همکارانه، یکی از موفقترین روشهای بهکاررفته در این سیستمها است که توانایی ارائۀ پیشنهادهایی نزدیک به نظر کاربران را دارد، اما با افزایش تعداد کاربران و محصولات، با مشکلاتی مانند شروع سرد و مقیاسپذیری مواجه میشوند. به همین دلیل در این پژوهش روش جدیدی معرفی شده است که ضمن بهکارگیری الگوریتم روش پالایش همکارانۀ مبتنی بر کاربر بهمثابۀ رویکرد پایه، از ترکیب وزندار خوشهبندی کاربران بر اساس اطلاعات جمعیتشناختی آنها نیز برای دستیابی به نتایج بهتر از سیستم، استفاده کرده است. نتایج پیادهسازی الگوریتم نشان داد رویکرد ارائهشده، ریشۀ میانگین مربعات خطای کمتری دارد که بهمعنای عملکرد بهتر و دقت بیشتر آن است و پیشبینیهای حاصل از آن با ترجیح و سلیقۀ کاربران همخوانی بیشتری دارد. | ||
کلیدواژهها [English] | ||
اطلاعات جمعیت شناختی, خوشهبندی, رویکرد پالایش همکارانه, سیستم پیشنهاددهنده | ||
مراجع | ||
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