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An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System

Thenmozhi Ganesan1 , Palanisamy Vellaiyan2

  1. Department of Computer Applications, Alagappa University, Karaikudi, India.
  2. Department of Computer Applications, Alagappa University, Karaikudi, India.

Section:Research Paper, Product Type: Journal Paper
Volume-11 , Issue-2 , Page no. 8-11, Feb-2023

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v11i2.811

Online published on Feb 28, 2023

Copyright © Thenmozhi Ganesan, Palanisamy Vellaiyan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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How to Cite this Paper

IEEE Style Citation: Thenmozhi Ganesan, Palanisamy Vellaiyan, “An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.2, pp.8-11, 2023.

MLA Style Citation: Thenmozhi Ganesan, Palanisamy Vellaiyan "An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System." International Journal of Computer Sciences and Engineering 11.2 (2023): 8-11.

APA Style Citation: Thenmozhi Ganesan, Palanisamy Vellaiyan, (2023). An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System. International Journal of Computer Sciences and Engineering, 11(2), 8-11.

BibTex Style Citation:
@article{Ganesan_2023,
author = {Thenmozhi Ganesan, Palanisamy Vellaiyan},
title = {An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2023},
volume = {11},
Issue = {2},
month = {2},
year = {2023},
issn = {2347-2693},
pages = {8-11},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5541},
doi = {https://doi.org/10.26438/ijcse/v11i2.811}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v11i2.811}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5541
TI - An Efficient Missing Data Prediction Technique using Recursive Reliability-Based Imputation for Book Recommendation System
T2 - International Journal of Computer Sciences and Engineering
AU - Thenmozhi Ganesan, Palanisamy Vellaiyan
PY - 2023
DA - 2023/02/28
PB - IJCSE, Indore, INDIA
SP - 8-11
IS - 2
VL - 11
SN - 2347-2693
ER -

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Abstract

Collaborative filtering recommender system is utilized as a significant method to suggest products to the users depends on their preferences. It is quite complicate when the user preference and rating data is sparse. Missing value occurs when there are no stored values for the specified dataset. Typical missing data are in three categories such as (i) Missing completely at random, (ii) Missing at random and (iii) Missing not at random. The missing values in dataset affect accuracy and causes deprived prediction outcome. In order to alleviate this issue, data imputation method is exploited. Imputation is the process of reinstating the missing value with substitute to preserve the data in dataset. It involves multiple approaches to evaluate the missing value. In this paper, we reviewed the progression of various imputation techniques and its limitations. Further, we endeavored k-recursive reliability-based imputation (k-RRI) to resolve the boundaries faced in existing approaches. Experimental results evince the studied methodology appreciably improves the prediction accuracy of recommendation system.

Key-Words / Index Term

Sparse Data, Missing Value, Recommendation system, Missing Value Imputation, Recursive Imputation, Prediction

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