Browse > Article
http://dx.doi.org/10.15207/JKCS.2020.11.11.019

Implementation of a pet product recommendation system using big data  

Kim, Sam-Taek (School of Information Technology Convergence, Woosong University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.11, 2020 , pp. 19-24 More about this Journal
Abstract
Recently, due to the rapid increase of pets, there is a need for an integrated pet-related personalized product recommendation service such as feed recommendation using a health status check of pets and various collected data. This paper implements a product recommendation system that can perform various personalized services such as collection, pre-processing, analysis, and management of pet-related data using big data. First, the sensor information worn by pets, customer purchase patterns, and SNS information are collected and stored in a database, and a platform capable of customized personalized recommendation services such as feed production and pet health management is implemented using statistical analysis. The platform can provide information to customers by outputting similarity product information about the product to be analyzed and information, and finally outputting the result of recommendation analysis.
Keywords
IoT; Recommendation System; Big Data; Data Analysis; Data Preprocessing;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen & X. Sun. (2010). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 559-569.   DOI
2 T. C. Fu. (2011). A Review on Time Series Data Mining. Engineering Application of Artificial Intelligence, 24(1), 164-181.   DOI
3 Y. Zhu, Y. Fu & H. Fu. (2010). A New Class of Attacks on Time Series Data Mining. Intelligent Data Analysis, 14(3), 405-418.   DOI
4 R. Agrawal & R. Srikant. (2000). Privacy-Preserving Data Mining. In Proc. of Conf. on Management of Data, ACM SIGMOD, Dallas, TX, 439-450.
5 S. T. Kim. (2016). Design of Convergence Platform for companion animal Personalized Services. Journal of the Korea Convergence Society, 7(6), 29-34. DOI : 10.15207/JKCS.2016.7.6.029   DOI
6 S. M. Bea & V. Torra. (2011). Trajectory Anonymization from a Time Series Perspective. In Proc. of IEEE Int'l Conf. on Fuzzy Systems, Taipei, Taiwan, 401-408.
7 S. H. Namn & K. S. Noh. (2015). A Study on the Effective Approaches to Big Data Planning. Journal of digital Convergence, 13(1), 227-235.   DOI
8 K. S. Noh. (2015). Convergence Analysis of Recognition and Influence on Big data in the e-Learning Field. Journal of digital Convergence, 13(10), 51-58.   DOI
9 H. Jung. (2015). The Analysis of Data on the basis of Software Test Data. Journal of digital Convergence, 13(10), 1-7.   DOI
10 C. Gurrin, A. F. Smeaton & A. R. Doherty. (2014). LifeLogging: Personal Big Data. Foundations and Trends in Information Retrieval, 8(2), 1-107.   DOI
11 S. Niwattanakul, J. Singthongchai, E. Naenudorn, & S. Wanapu. (2013). Using Jaccard Coefficient for Keywords Similarity. Proceedings of the International MultiConference of Engineers and Computer Scientists, 1(2202), 380-384.
12 G. Karypis. (2001). Evaluation of Item-Based Top-N Recommendation Algorithms. Proc. of CIKM '01 Conference, 247-254.
13 J. L Herlocker, J. A. Konstan, L. G. Terveen & J. Riedl. (2001). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 22(1), 5-53.   DOI
14 S. S. Weng & M. J. Liu. (2004). Feature-based recommendations for one-to-one marketing. Expert Systems with Applications,26(4),493-508.   DOI
15 J. Horey, E. Begoli, R. Gunasekaran, S. Lim & J. Nutaro (2012). Big Data Platforms as a Service: Challenges and Approach. USENIX Workshop on Hot Topics in Cloud Computing (HotCloud). https://www.usenix.org/system/files/conference/hotcloud12/hotcloud12-final61.pdf