GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

Keywords: customer preferences, rating, recommendation, RFM, K-Means.

Abstract

A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts.

Downloads

Download data is not yet available.

References

Hongbing Wang, Shizhi Shao, Xuan Zhou, Cheng Wan, Athman Bouguettaya, Preference recommendation for personalized search, Knowledge-Based Systems, 2016. DOI: https://doi.org/10.1016/j.knosys.2016.02.016

Kai Zhang, Keqiang Wang, Xiaoling Wang, Cheqing Jin, Aoying Zhou, Hotel Recommendation based on User Preference Analysis, pp. 134-138, 2015. DOI: https://doi.org/10.1109/ICDEW.2015.7129564

Le Hoang Son, HU-FCF++: A novel hybrid method for the new user cold-start problem in recommender systems, Engineering Applications of Artificial Intelligence, Vol. 41, pp. 207-222, 2015. DOI: https://doi.org/10.1016/j.engappai.2015.02.003

Syafrial Fachri Pane, Rolly Maulana Awangga, Bayu Rahmad Azhari, Qualitative Evaluation of RFID Implementationon Warehouse Management System, TELKOMNIKA (Telecommunication Comput. Electron. Control, Vol. 16, 2018. DOI: https://doi.org/10.12928/telkomnika.v16i3.8400

Rolly Maulana Awangga, Syafrial Fachri Pane, Khaera Tunnisa, Iping Supriana Suwardi, K Means Clustering and Meanshift Analysis for Grouping the Data of Coal Term in Puslitbang tekMIRA, TELKOMNIKA (Telecommunication Comput. Electron. Control, Vol. 16, 2018. DOI: https://doi.org/10.12928/telkomnika.v16i3.8910

Keng-Pei Lin, Chia-Yu Lai, Po-Cheng Chen, San-Yih Hwang, Personalized Hotel Recommendation Using Text Mining and Mobile Browsing Tracking, IEEE International Conference on System, Man, and Cybernetics, pp. 191-196, 2016.

Kanae Matsui, Hanjong Choi, A recommendation system with secondary usage of HEMS data for products based on IoT technology, International Symposium on Networks, Computers and Communications, ISNCC, 2017. DOI: https://doi.org/10.1109/ISNCC.2017.8071982

Imran Memon, Ling Chen, Abdul Majid, Mingqi Lv, Ibrar Hussain, Gencai Chen, Travel Recommendation Using Geo-tagged Photos in Social Media for Tourist, Wireless Personal Communications, Vol. 80, No.4, pp. 1347-1362, 2015.

Idir Benouaret, Dominique Lenne, A Composite Recommendatio System for Planning Tourist Visits, International Conference on Web Intelligence, pp. 626-631, 2016. DOI: https://doi.org/10.1109/WI.2016.0110

Blake Hallinan, Ted Striphas, Recommended for you : The Netflix Prize and the production of algorithmic culture, New Media & Society, Vol. 18, No. 1, pp.117-137, 2016. DOI: https://doi.org/10.1177/1461444814538646

Jenet Manyi Agbor, The Relationship between Customer Satisfaction and Service Quality: a study of three Service sectors in Umea, UMEA Universiti, 2011.

William Wei Song, Chenlu Lin, Anders Forsman, Anders Avdic, Leif Akerblom, An Euclidean Similarity Measurement Approach for Hotel Rating Data Analysis, IEEE 2nd International Conference on Cloud Computing and Big Data Analysis, pp. 293-298, 2017.

Koji Takuma, Junya Yamamoto, Sayaka Kamei, Satoshi Fujta, A hotel recommendation system based on reviews: What do you attach importance to?, Fourth International Symposium on Computing and Networking, pp. 710–712, 2017. DOI: https://doi.org/10.1109/CANDAR.2016.0129

Huiming Wang, Nianlong Luo, Collaborative filtering enhanced by user free-text reviews topic modelling, 2014. DOI: https://doi.org/10.1049/cp.2014.0584

Anbazhagan Mahadevan, Michael Arock, Credible User-Review Incorporated Collaborative Filtering for Video Recommendation System, International Conference on Intelligent Sustainable Systems (ICISS), pp. 375-379, 2017. DOI: https://doi.org/10.1109/ISS1.2017.8389433

Shahriar Badsha, Xun Yi, Ibrahim Khalil, A practical privacy-preserving recommender system, Data Science and Engineering, Vol. 1, pp. 161–177, 2016. DOI: https://doi.org/10.1007/s41019-016-0020-2

Zhou Zhao, Deng Cai, Xiaofei He, Yueting Zhuang, User Preference Learning for Online Social Recommendation, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 9, pp. 2522-2534, 2016.

Zhou Zhao, Qifan Yang, Hanqing Lu, Tim Weninger, Deng Cai, Xiaofei He, Yueting Zhuang, Social-Aware Movie Recommendation via Multimodal Network Learning, IEEE Transactions on Multimedia, Vol. 20, No. 2, pp. 430-440, 2018. DOI: https://doi.org/10.1109/TMM.2017.2740022

Ihsan Topalli, Selcuk Kilinc, Modelling User Habits and Providing Recommendations based on the Hybrid Broadcast Broadband Television using Neural Networks, IEEE Transactions on Consumer Electronics, Vol. 62, no. 2, pp. 182–190, 2016. DOI: https://doi.org/10.1109/TCE.2016.7514718

K. Kesorn, W. Juraphanthong, A. Salaiwarakul, Personalized Attraction Recommendation System for Tourists Through Check-In Data, IEEE Access, vol. 5, pp. 26703–26721, 2017.

Xueming Qian, He Feng, Guoshuai Zhao, Tao Mei, Personalized Recommendation Combining User Interest and Social Circle, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No.7, pp. 1763–1777, 2014.

Zhiyang Jia, Wei Gao, Yuting Yang, Xu Chen, User-based Collaborative Filtering for Tourist Attraction Recommendations, IEEE International Conference on Computational Intelligence & Communication Technology (CICT), pp. 22-25, 2015.

Junge Shen. Cheng Deng, Xinbo Gao, Neurocomputing Attraction recommendation : Towards personalized tourism via collective intelligence, Neurocomputing, Vol. 173, pp. 789-798, 2016. DOI: https://doi.org/10.1016/j.neucom.2015.08.030

Michalis Korakakis, Phivos Mylonas, Evaggelos Spyrou, Xenia : A Context Aware Tour Recommendation System Based on Social Network Metadata Information, 11th International Workshop on Semantic and Social Media Adaption and Personalization (SMAP), pp. 59-64, 2016. DOI: https://doi.org/10.1109/SMAP.2016.7753385

Chin-I Lee, Tse-Chih Hsia, Hsiang-Chih Hsu, Jing-Ya Lin, Ontology-Based Tourism Recommendation System, 4th International Conference on Industrial Engineering and Applications, pp. 376-379, 2017.

Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, Shanika Karunasekera, Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations, International Joint Conference on Artificial Intelligece (IJCAI), Vol. 15, pp. 1778-1784, 2015.

Chenyi Zhang, Ke Wang, POI recommendation through cross-region collaborative filtering, Knowledge and Information Systems, Vol. 46, No. 2, pp. 369-387, 2017. DOI: https://doi.org/10.1007/s10115-015-0825-8

Rachid Aid Daoud, Belaid Bouikhalene, Abdellah Amine, Rachid LBIBB, Combining RFM model and clustering techniques for customer value analysis of a company selling online, IEEE 12th International Conference of Computer Systems and Applications (AICCSA), pp. 1-6, 2015. DOI: https://doi.org/10.1109/AICCSA.2015.7507238

Jo Ting Wei, Shih-Yen Lin, You-Zhen Yang, Hsin-Hung Wu, Applying data mining and RFM model to analyze customers’ values of a veterinary hospital, International Symposium on Computer, Consumer and Control, pp. 481-484, 2016.

Kristof Coussement, Filip A.M. Van den Bossche, Koen W. De Bock, Data accuracy’s impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees, Journal of Business Research, Vol. 67, No. 1, pp. 2751-2758, 2014.

Ferdi Yusuf, Pembangunan Sistem Informasi Customer Relationship Management di Koperasi Pegawai dan Pensiunan PT. Pos Indonesia (KOPPOS), Jurnal Ilmiah Komputer dan Informatika (KOMPUTA), Vol. 1, No. 1, 2016.

Michele Amoretti, Laura Belli, Francesco Zanichelli, UTravel : Smart mobility with a novel user profiling and recommendation approach, Pervasive and Mobile Computing, Vol. 38, pp. 474-489, 2017. DOI: https://doi.org/10.1016/j.pmcj.2016.08.008

Merlinda Sumardi, Jufery, Frenky, Rini Wongso, Ferdinand Ariandy Luwinda, “TripBuddy†Travel Planner with Recommendation based on User ‘ s Browsing Behaviour, International Conference on Computer Science and Computational Intelligence (ICCSCI), Vol. 116, pp. 326-333, 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.084

Shaoqing Wang, Cuiping Li, Kankan Zhao, Hong Chen, Context-Aware Recommendations with Random Partition Factorization Machines, Data Science and Engineering, Vol. 2, No. 2, pp. 125-135, 2017. DOI: https://doi.org/10.1007/s41019-017-0035-3

Published
2019-12-01
How to Cite
Awangga, R. M., Pane, S. F., & Wijayanti, D. A. (2019). GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM. EMITTER International Journal of Engineering Technology, 7(2), 404-422. https://doi.org/10.24003/emitter.v7i2.325
Section
Articles