A Comparative Analysis of Similarity Metrics on Sparse Data for Clustering in Recommender Systems

2018 ◽  
pp. 291-299 ◽  
Author(s):  
Rodolfo Bojorque ◽  
Remigio Hurtado ◽  
Andrés Inga
Keyword(s):  
Comparative Analysis ◽  
Recommender Systems ◽  
Sparse Data ◽  
Similarity Metrics ◽  
Analysis Of Similarity

Study and Comparative Analysis of Existing Recommender Systems

2019 ◽  
Vol 7 (1) ◽  
pp. 262-266
Author(s):  
Sanjay . ◽  
Yogesh Kumar ◽  
Rahul Rishi
Keyword(s):  
Comparative Analysis ◽  
Recommender Systems

Three-way formal concept clustering technique for matrix completion in recommender system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chemmalar Selvi G. ◽  
Lakshmi Priya G.G.
Keyword(s):  
Recommender Systems ◽  
Error Rate ◽  
Prediction Accuracy ◽  
Matrix Completion ◽  
Sparse Data ◽  
Data Matrix ◽  
Formal Concept ◽  
Data Loss ◽  
Content Type ◽  
The Matrix

Purpose In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information. Design/methodology/approach To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix. Findings The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score. Social implications The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products. Originality/value This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

Similarity Metrics from Social Network Analysis for Content Recommender Systems

2016 ◽  
pp. 203-217 ◽  
Author(s):  
Guillermo Jimenez-Diaz ◽  
Pedro Pablo Gómez Martín ◽  
Marco Antonio Gómez Martín ◽  
Antonio A. Sánchez-Ruiz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Recommender Systems ◽  
Similarity Metrics
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