A New Similarity Method to Optimize Business in the Online Stores Using the Rating Time Technology

Document Type : Research Paper

Authors

1 Ph.D. Candidate in Computer Engineering, Azad University, Tehran, Iran

2 Ph.D. Candidate in Business Management, Tehran University, Tehran, Iran

Abstract

These days, Emergence of e-commerce web sites is one of the important consequences of the Internet in modern times, but products data is growing exponentially. In such environment, customers face a problem in finding optimized information among huge data bases about the items or desired products. In order to assist buyers, large e-commerce companies are planning to introduce their own recommender systems to help their customers in making a better choice among the items. Due to high percentage error , a basic method to build such systems is not usually being applied. In this essay, two methods have been suggested in order to improve recommendations in recommender systems. Collaborative filtering method is one of the most successful methods used in the system, but using this method that it has common problem the increasing number of users and products, therefore system do not inability to request the requirement of cold start and data sparsity. Two methods have been suggested in order to improve recommendations in recommender systems. To resolve this problem, a new method has been introduced in which by integrating  rating time by Pearson also combining semantic technology with social networks offers a solution to reduce issues such as "cold start" and generally "data sparsity" in recommender systems. The result of simulating showed that the proposed approach provided better performance and more accurate predictions in addition of more consistent with user preferences.

Keywords

Main Subjects


حسنقلی‎پور، ط.؛ امیری، م.؛ فهیم، ف.؛ قادری عابد، ا.ح. (1392). بررسی تأثیر خصوصیات مشتریان بر تمایل آنها به پذیرش خرید اینترنتی (پیمایشی پیرامون دانشکدۀ مدیریت دانشگاه تهران). نشریۀ مدیریت فناوری اطلاعات، 5(4)، 84-67.
کرامتی، ع.؛ خالقی، ر. (1393). توسعۀ یک سیستم پیشنهاددهندة محصول طراحی مدل ترکیبی با بهره‌گیری از روش‌های فیلترینگ مشارکت‌محور، کشف قوانین انجمنی، و بخش‌بندی مشتریان. نشریۀ مهندسی صنایع، 48 (2): 280- 257.
کریمی علویجه، م. ح.؛ عسگری، ش.؛ پرسته، س. (1394). فروشگاه اینترنتی هوشمند: سیستم پیشنهاددهندۀ مبتنی بر تحلیل رفتار کاربران. نشریۀ مدیریت فناوری اطلاعات، 7 (2)، 406-385.
کی‎پور، ا.؛ براری، م. و شیرازی، ح. (1393). ارائۀ روشی جدید برای پیشگویی پیوند بین رأس‎های موجود در شبکه‎های اجتماعی. فصلنامۀ مدیریت فناوری اطلاعات، 6 (3)، 486- 475.
Candillier, L., Meyer, F. & Fessant, F. (2008). Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems. in Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. vol. 5077, P. Perner, Ed., ed: Springer Berlin Heidelberg, pp. 242-255.
Gopal, R.D., Tripathi, A.K., Walter, Z.D. (2006). Economics of first-contact e-mail advertising. Decision Support System, 42(3),1366–1382.
Hasan Gholipour, T., Amiri, M., Fahim, F. & Ghaderi abed, A. (2013). Effect of customer characteristics on their willingness to adopt Internet shopping. Quarterly Journal of Information technology management, 5(4), 67-84.
(in Persian)
Hill, W., Stead, L., Rosenstein, M. & Furnas, G. (1995). CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, Colorado, USA - May 07 - 11, Pages 194-201.
Karimi, M., Askari, SH. & Paraste, S. (2015). Intelligent Internet store: Recommended system based on analysis of user behavior, Quarterly Journal of Information technology management, 7(2), 385-406.
(in Persian)
Keramati, A. & Khaleghi, R. (2013). Developing a design product recommender system utilizes a hybrid model based collaborative filtering methods, the discovery of association rules and customer segmentation. Specialist Journal of industrial manageent, 48 (2), 257-280. (in Persian)
Keypour, A., Barari, M. & Shirazi, H. (2014). A new method for predict links between nodes in social networks. Quarterly Journal of Information technology management, 6(3), 475-486. (in Persian)
Lee, T. Q., Park, Y. & Park, Y. T. (2008). A time-based approach to effective recommender systems using implicit feedback. Expert systems with applications, 34(4), 3055-3062.
O'Donovan, J. & Smyth, B. (2005). Trust in recommender systems. presented at the Proceedings of the 10th international conference on Intelligent user interfaces, San Diego, California, USA.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. & Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. presented at the Proceedings of the 1994 ACM conference on Computer supported cooperative work. Oct 22 (pp. 175-186). ACM.
Sarwar, B., Karypis, G. Konstan, J. & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms, presented at the Proceedings of the 10th international conference on World Wide Web, Hong Kong, Hong Kong.
Shambour, Q. & Lu, J. (2012). A trust-semantic fusion-based recommendation approach for e-business applications. Decision Support Systems, 54(1), 768-80.
Shardanand, U. & Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth”. presented at the Proceedings of the SIGCHI conference on Human factors in computing systems.1995 May 1 (pp. 210-217). ACM Press/Addison-Wesley Publishing Co.
Shinde, S.K. & Kulkarni, U. (2012). Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Systems with Applications, 39(1), 1381-1387.
Zamani, A., Rahmati, M. H. (2014). Identifying and Rating Affecting Factors on Business Process Management (Bpm) Successful Execution in Iranian Insurance Companies by Analytical Hierarchical Process (Ahp) Technique. Journal of Social Issues & Humanities, 2(11), 121-127.