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Developing a Product Recommender System: Designing a Hybrid Model Using Data Mining Techniques | ||
| Advances in Industrial Engineering | ||
| مقاله 10، دوره 48، شماره 2، دی 2014، صفحه 257-280 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jieng.2014.52918 | ||
| نویسندگان | ||
| Abbas Keramati؛ Roshanak Khaleghi* | ||
| School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, I.R. Iran | ||
| چکیده | ||
| The rapid growth of World Wide Web has affected the nature of interactions between customers and companies enormously. One significant consequence of this phenomenon is definitely the emergence and development of e-commerce websites and online stores all over the web. In spite of its great benefits, online shopping could turn into a complicated procedure from the customer point of view. In most cases, online shoppers are faced with overload of information related different products and services; as a result, deciding which products or services best fit their needs, may become a difficult or even a time consuming process. Recommender systems help online shoppers handle the information overload problem by offering products or services in accordance with their preferences. The application of recommender systems, as a part of one-to-one marketing campaigns, would facilitate the product selection process, provide more customer satisfaction and could eventually increase the sales of e-commerce websites. This paper develops a product recommender system for the users of an online retail store by using data mining techniques. First, customers are clustered according to their “RFM” values considering their relative preferences over different product categories by means of “k-means” algorithm. Then, by applying a two-phase recommendation methodology which is based on a hybrid of “association rule mining” and “collaborative filtering” techniques, the system offers the list of recommendations to target customers at two different levels of product taxonomy, respectively “product categories” and “product items”. The experimental results show that, by alleviating data Sparsity and scalability limitations, the proposed recommender model has a better performance compared to some other similar models such as models which are developed based on the conventional collaborative filtering technique. The results of this research could be effectively used to accomplish the objectives of one-to-one marketing campaigns and develop personalized product recommendation strategies for different customer segments of E-commerce websites regarding their lifetime value. | ||
| کلیدواژه‌ها | ||
| Clustering؛ Collaborative Filtering (CF)؛ Association Rule Mining (ARM)؛ Customer Lifetime Value (CLV)؛ Data Mining | ||
| عنوان مقاله [English] | ||
| توسعة یک سیستم پیشنهاددهندة محصول طراحی مدلی ترکیبی با بهره‌گیری از روش‌های فیلترینگ مشارکت‌محور، کشف قوانین انجمنی، و بخش‌بندی مشتریان | ||
| نویسندگان [English] | ||
| عباس کرامتی؛ روشنک خالقی | ||
| دانشیار دانشکدة مهندسی صنایع پردیس دانشکده‌های فنی دانشگاه تهران | ||
| چکیده [English] | ||
| توسعة روزافزون اینترنت نحوة تعاملات مشتریان و سازمانها را دستخوش تحولات چشمگیری کرده است. یکی از پیامدهای مهم این پدیده پیدایش و گسترش وبگاههای تجارت الکترونیکی و افزایش گرایش کاربران به بهره‌گیری از خدمات خریدوفروش برخط است. تنوع خدمات و اقلام عرضه‌شده در این وبگاهها میتواند انتخاب محصولات مناسب را برای مشتریان به فرایندی پیچیده و زمانبر مبدل کند. سیستمهای پیشنهاددهنده، با شناسایی ترجیحات مشتریان، آنان را در مواجهه با انبوه اطلاعات یاری و محصولات و خدماتی منطبق سلایقشان به آن‌ها ارائه می‌کند. هدف این پژوهش ارائة مدلی برای توسعة یک سیستم پیشنهاددهندة محصول به مشتریان یک خردهفروشی برخط، با بهره‌گیری از مجموعهای از روش‌های دادهکاوی، است. با استناد به چارچوب پیشنهادی مدل، نخست مشتریان با تکیه بر رویکرد بخشبندیِ مبتنی بر ارزشِ طول عمر و با لحاظ‌کردن نسبیِ ترجیحات، بر اساس مشخصههای مدل RFM، خوشهبندی میشوند. سپس با بهره‌گیری از ساختار پیشنهاددهیِ دومرحلهای پیشنهادهای گوناگون در دو سطح متمایز از ردهبندی محصول به هر یک از مشتریان هدف ارائه می‌شود. در مرحلة نخست، با بهره‌گیری از روش کشف قوانین انجمنی، تراکنش‌های مشتریان هر خوشه در سطح کلاس محصولات بررسی و با شناساییِ الگوها و وابستگیهای پنهان در دادهها قوانین پیشنهاددهیِ معتبر استخراج و لیستی متشکل از کلاس‌محصولات پیشنهادی به هر یک از مشتریان هدف ارائه می‌شود. در مرحلة دوم با بهره‌گیری از رویکرد فیلترینگ مشارکت‌محور و با استناد به خروجیهای مرحلة پیش ترجیحات خریدِ مشتریان در سطح اقلامِ کلاس‌محصولات پیشنهادی شناسایی و لیست نهایی اقلام محصول به هر یک از مشتریان هدف پیشنهاد می‌شود. نتایج اجرای مدل حاکی از آن است که مدل پیشنهادی با بهره‌گیری از یک رویکرد ترکیبیِ پیشنهاددهی و با کاهش معضلات ناشی از دو پدیدة عدم تراکم و مقیاسپذیری، در قیاس با مدلهای مشابهی که با تکیه بر رویکرد سنتی فیلترینگ مشارکت‌محور توسعه مییابند، عملکرد مطلوبتری دارد. | ||
| کلیدواژه‌ها [English] | ||
| ارزش طول عمر, خوشهبندی, دادهکاوی فیلترینگ مشارکت‌محور, قوانین انجمنی | ||
| مراجع | ||
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