Adaptive Clustering of Merchandise Code for Recommender System with an Immediate Effect
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-10-0281-6_83
Reference9 articles.
1. Cho, Y.S., Kim, K.A., Moon, S.C., Park, S.H., Ryu, K.H.: Effective purchase pattern mining with weight based on FRAT analysis for recommender in e-commerce. In: Computer Science and its Applications, pp. 443–454 (2015)
2. Khajvand, M., Zolfaghar, K., Ashoori, S., Alizadeh, S.: Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science 3, 57–63 (2011)
3. Cho, Y.S., Moon, S.C.: Weighted mining frequent itemsets using FP-tree based on RFM for personalized u-commerce recommendation system. In: Mobile, Ubiquitous, and Intelligent Computing, pp. 441–450 (2014)
4. Cho, Y.S., Moon, S.C., Noh, S.C., Ryu, K.H.: Implementation of personalized recommendation system using k-means clustering of item category based on RFM. In: IEEE International Conference on Management of Innovation and Technology (ICMIT), pp. 378–383 (2012)
5. Jung, K.Y.: User preference through learning user profile for ubiquitous recommendation systems. In: Knowledge-Based Intelligent Information and Engineering Systems, pp. 163–170 (2006)
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