Research on User Segmentation based on RFL Model and K-means Clustering Algorithm
- 10.2991/lemcs-15.2015.298How to use a DOI?
- User; Segmentation; RFL; K-means; Clustering
With the rapidly shifting dynamics of the current market, the companies are seeking a more thorough method to research the preferences of their target market. As such, data mining models of user segmentation are often utilized to fill up the broadcasting and television research areas. This paper proposes a broadcasting and television RFL model for channel user segmentation and then gives the model for typical use case. The model has two main advantages, showing the users’ value dynamically and having strong data availability together with wide model applicability. To define users’ degree of satisfaction towards diverse television channel, R, F and L indicators are built. Then this paper uses the optimized k-means algorithm to divide users into clusters, along with cross validation by two-step clustering, which helps verify the results. By comparing each user cluster’s average R, F and L indicators with the ensample mean, users can be subdivided into six levels: key-growth user, key-development user, general-growth user, key-kept user, low-value user and general user. On this basis, recommendations are given to the broadcasting and television operators and advertisers to assist them to make profits.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yunpeng Chen AU - Ziyu Liu AU - Yan Wang AU - Yao Qin PY - 2015/07 DA - 2015/07 TI - Research on User Segmentation based on RFL Model and K-means Clustering Algorithm BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 1499 EP - 1503 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.298 DO - 10.2991/lemcs-15.2015.298 ID - Chen2015/07 ER -