Addressing the Cold-Start Problem Using Data Mining Techniques and Improving Recommender Systems by Cuckoo Algorithm: A Case Study of Facebook

2020 ◽  
Vol 22 (4) ◽  
pp. 62-73 ◽  
Author(s):  
Saman Forouzandeh ◽  
Atae Rezaei Aghdam ◽  
Soran Forouzandeh ◽  
Shuxiang Xu

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.


2022 ◽  
pp. 24-56
Author(s):  
Rajab Ssemwogerere ◽  
Wamwoyo Faruk ◽  
Nambobi Mutwalibi

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.


Author(s):  
Dayana Vila ◽  
Saúl Cisneros ◽  
Pedro Granda ◽  
Cosme Ortega ◽  
Miguel Posso-Yépez ◽  
...  

Author(s):  
Syaidatus Syahira Ahmad Tarmizi ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul Hamid ◽  
Shuzlina Abdul-Rahman ◽  
Ariff Md Ab Malik

2018 ◽  
Vol 05 (02) ◽  
pp. 145-153
Author(s):  
Fatemeh Sadeghi Laghareh ◽  
Seyed Javad Mirabedini ◽  
Ali Haroon Abadi

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