پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,111,899 |
| تعداد دریافت فایل اصل مقاله | 97,215,543 |
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 | ||
|
1. E. W. T. Ngai, Li Xiu, Chau, D. C. K. (2009). "Application of data mining techniques in customer relationship management: A literature review and classification." Expert Systems with Applications, Vol. 36, 2592–2602.
2. T. Jiang and A. Tuzhilin. (2006). "Segmenting customers from population to individuals: Does 1-to-1 keep your customers forever." IEEE Transactions on Knowledge and Data Engineering, Vol. 18, 1297–1311.
3. S. K. Shinde and U. Kulkarni. (2012). "Hybrid personalized recommender system using centering-bunching based clustering algorithm." Expert Systems with Applications, Vol. 39, 1381–1387.
4. L. M. d. Campos, J. M. Fernández-Luna, J. F. Huete, Miguel A. Rueda-Morales. (2010). "Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks." International Journal of Approximate Reasoning, Vol. 51, 785–799.
5. D. R. Liu, C. Lai, W.J. Lee. (2009). "A hybrid of sequential rules and collaborative filtering for product recommendation." Information Sciences, Vol. 179, 3505-3519.
6. L. S. Chen, F.H. Hsu, M.Ch. Chen, Y.Ch Hsu (2008). "Developingrecommender systems with the consideration of product profitability for sellers." Information Sciences, Vol. 178, 1032–1048.
7. G. Adomavicius and A. Tuzhilin. (2005). "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Artand Possible Extensions." IEEE Transactions On Knowledge And Data Engineering, Vol. 17, 734-749.
8. R. Burke. (2002). "Hybrid recommender systems: survey and experiments." User Modeling and User-Adapted Interaction, Vol. 12, 331-370.
9. L. Lu, M. Medo, Ch.H Yeung, Y.Ch. Zhanga, Z.K. Zhang, T. Zhoua. (2012). "Recommender Systems." Physics Reports.
10. D. H. Park, H.K Kim, Y. Choi, J.K Kim. (2012). "A literature review and classification of recommender systems research." Expert Systems with Applications, Vol. 39, 10059-10079.
11. S. H. Choi, S. Kang, Y.J Jeon. (2006). "Personalized recommendation system based on product specification values." Expert Systems with Applications, Vol. 31, 607–616.
12. P. Resnick. (1994). "Grouplens: an open architecture for collaborative filtering on netnews." in Proc. conference on computer supported cooperative work, 175-186.
13. J. B. Schafer, J.A. Konstan, J. Riedl. (2001) "E-commerce recommendation applications." Data Mining and Knowledge Discovery, Vol. 5, 115-153.
14. H. F. Wang and C. T. Wu. (2012). "A strategy-oriented operation modulefor recommender systems in E-commerce." Computers & Operations Research, Vol. 39, 1837–1849.
15. S. H. Choi and B. S. Ahn. (2011). "Rank order-based recommendation approach for multiple featured products." Expert Systems with Applications, Vol. 38, 7081-7087.
16. M. Balabanovic and Y. Shoham. (1997). "Fab: Content-based, Collaborative Recommendation." Communications of the ACM, Vol. 40, 66-72.
17. K. Lang. (1995). "Newsweeder: Learning to filter news," in Proc. 12th International Conference on Machine Learning, 331-339.
18. H. F. Wang and C. T. Wu. (2009). "A mathematical model for product selection strategies in a recommender system." Expert Systems with Applications, Vol. 36, 7299–7308.
19. M. Montaner, B. Lopez and J. L. DE LA ROSA. (2003). "A Taxonomy of Recommender Agents on the Internet." Artificial Intelligence Review, Vol. 19, 285-330.
20. S. L. Huang. (2011). "Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods." Electronic Commerce Research and Applications, Vol. 10, 398–407.
21. Y. F. Wang, Y.L Chuang, M.H Hsu, H.Ch Keh. (2004). "A personalized recommender system for the cosmetic business." Expert Systems with Applications, Vol. 26, 427–434.
22. W. Yang and J. Dia. (2008). "Discovering cohesive subgroups from social networks for targeted advertising." Expert Systems with Applications, Vol. 34, 2029–2038.
23. A. Albadvi and M. Shahbazi. (2010). "Integrating rating-based collaborative filtering with customer lifetime value: New product recommendation technique".Intelligent Data Analysis, Vol. 14, 143-155.
24. B. Sarwar, G. Karypis, J. Konstan, J. Riedl. (2000). "Analysis of recommendation algorithms for e-commerce." in Proc. 2nd ACM Conference on Electronic Commerce, 158–167.
25. Y. Y. Shih and D. R. Liu. (2008). "Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands." Expert Systems with Applications, Vol. 35, 350-360.
26. J. S. Breese. (1998). "Empirical analysis of predictive algorithms for collaborative filtering".in Proc. 14th Annual Conference on Uncertainty in Artificial Intelligence, 43-52.
27. D. C. Li, W.L Dai, W.T Tseng. (2011). "A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business." Expert Systems with Applications, Vol. 38, 7186–7191.
28. S. M. S. Hosseini, A. Maleki, M.R Gholamian. (2010). "Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty." Expert Systems with Applications, Vol. 37, 5259–5264.
29. S. Y. Kim, T.S Jung, E.H. Suh, H.S. Hwang. (2006). "Customer segmentation and strategy development based on customer lifetime value: A case study." Expert Systems with Applications, Vol. 31, 101-107.
30. S. S. R. Abidi and J. Ong. (2000). "A Data Mining Strategy for Inductive Data Clustering: A Synergy Between Self-Organising Neural Networks and K-Means Clustering Techniques." in Proc. IEEE TENCON, 568-573.
31. C. H. Cheng and Y. S. Chen. (2009). "Classifying the segmentation of customer value viaRFM model and RS theory." Expert Systems with Applications, Vol. 36, 4176–4184.
32. D. Jain and S. S. Singh. (2002). "Customer lifetime value research in marketing: a review and future directions." Journal Of Interactive Marketing, Vol. 16, 34-46.
33. J. T. Wei, S.Y. Lin, Ch.Ch. Weng, H.H. Wu. (2012). "A case study of applying LRFM model in market segmentation of a children’s dental clinic." Expert Systems with Applications, Vol. 39, 5529–5533.
34. D. R. Liu and Y. Y. Shih. (2005). "Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences." The Journal of Systems and Software, Vol. 77, 181-191.
35. D. R. Liu and Y. Y. Shih. (2005). "Integrating AHP and data mining for product recommendation based on customer lifetime value".Information & Management, Vol. 42, 387-400.
36. G. T. S. Ho, W.H. Ip, C.K.M. Lee, W.L. Mou. (2012). "Customer grouping for better resources allocation using GA based clustering technique." Expert Systems with Applications, Vol. 39, 1979–1987.
37. S. Mitra. (2002). "Data mining in soft computing framework: A survey." IEEE Transactions on Neural Networks, Vol. 13, 3-14.
38. J. Han and M. Kamber. (2005)."Data Mining Concepts and Techniques", 2nd ed. San Francisco: The Morgan Kaufmann Series in Data Management Systems.
39. M. Brun. (2007). "Model-based evaluation of clustering validation measures." Pattern Recognition, Vol. 40, 807–824.
40. Y. S. Kim and B. J. Yum. (2011). "Recommender system based on click stream data using association rule mining".Expert Systems with Applications, Vol. 38, 13320–13327.
41. W. Y. Chiang. (2011). "To mine association rules of customer values via a data mining procedure with improved model: An empirical case study." Expert Systems with Applications, Vol. 38, 16-17, 1722.
42. R. Agrawal, T. Imielinski, A. Swami. (1993). "Mining Association Rules between Sets of Items in Large Databases." in Proc.1993 ACM SIGMOD Conference on Management of data, 207-216.
43. Y. H. Cho and J. K. Kim. (2004). "Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce." Expert Systems with Applications, Vol. 26, 233–246.
44. Y. J. Park and K. N. Chang. (2009). "Individual and group behavior-based customer profile model for personalized product recommendation." Expert Systems with Applications, Vol. 36, 1932–1939. | ||
|
آمار تعداد مشاهده مقاله: 2,969 تعداد دریافت فایل اصل مقاله: 2,146 |
||