Research Article
A FCA-Based Concept Clustering Recommender System
@INPROCEEDINGS{10.1007/978-3-030-34365-1_14, author={G. Chemmalar Selvi and G. Lakshmi Priya and Rose Joseph}, title={A FCA-Based Concept Clustering Recommender System}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2019}, month={12}, keywords={Recommender system Collaborative filtering Clustering Formal concept analysis Sparsity}, doi={10.1007/978-3-030-34365-1_14} }- G. Chemmalar Selvi
G. Lakshmi Priya
Rose Joseph
Year: 2019
A FCA-Based Concept Clustering Recommender System
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-34365-1_14
Abstract
Recommender systems are information filtering software which is capable of resolving the recent issue of internet’s information overload. The recommender system generate the recommendation more suitably based on the data gathered either implicitly like user profile, click information, web log history or explicitly like ratings (scale 1–5), likes, dislikes, feedbacks. The most important challenge to the recommender system is the growing number of online users making it a high dimensional data which leads to the data sparsity problem where the accuracy of recommendation depends on the availability of the data. In this paper, a new approach called formal concept analysis is employed to handle the high dimensional data and a FCA-based recommender algorithm, User-based concept clustering recommendation algorithm (UBCCRA) is proposed. The UBCCRA out performs by accurately generating the recommendation for the group-based users called cluster users. The experimental result is shown to prove the cluster recommendation with good result.