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A FCA-Based Concept Clustering Recommender System

  • G. Chemmalar SelviEmail author
  • G. G. Lakshmi Priya
  • Rose Bindu Joseph
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 298)

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.

Keywords

Recommender system Collaborative filtering Clustering Formal concept analysis Sparsity 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • G. Chemmalar Selvi
    • 1
    Email author
  • G. G. Lakshmi Priya
    • 1
  • Rose Bindu Joseph
    • 2
  1. 1.Vellore Institute of TechnologyVelloreIndia
  2. 2.Christ Academy Institute for Advanced StudiesBengaluruIndia

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