Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)

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Identifying MCCF Vulnerabilities and Preparing Datasets for Effective Analysis: A Comprehensive Approach

Authors
Shraddha Soni1, *, Sunita Varma2
1I.I.P.S, D.A.V.V, Indore, India
2S.G.S.I.T.S, Indore, India
*Corresponding author. Email: shraddha.soni@iips.edu.in
Corresponding Author
Shraddha Soni
Available Online 28 May 2026.
DOI
10.2991/978-94-6239-678-4_21How to use a DOI?
Keywords
Recommender system; Multi-criteria collaborative filtering (MCCF); vulnerabilities; shilling attacks; collaborative filtering; datasets
Abstract

Recommender system has added an incredible convenience by delivering suggestions that are of our interest while we dive into the ocean of information. Among different recommender techniques, Multi-criteria collaborative filtering (MCCF)system is the one who determines user preferences by carefully considering the user rating on multiple criteria of an item. As the system is dependent on explicit ratings given by user, this openness may make the entire system vulnerable for shilling attacks, thereby causing malicious users to corrupt the credibility of system by proving fake ratings. Identification of all such vulnerabilities within these systems provides worthwhile insight for the security and robustness of this recommender system. This study aims to identify various vulnerabilities of MCCF and construction of multiple datasets for specific analytic requirements. The finding underpins measures that are to be taken for building a robust system and the datasets constructed can be used to analyze and study shilling attacks on MCCF more precisely in future researches.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)
Series
Advances in Intelligent Systems Research
Publication Date
28 May 2026
ISBN
978-94-6239-678-4
ISSN
1951-6851
DOI
10.2991/978-94-6239-678-4_21How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shraddha Soni
AU  - Sunita Varma
PY  - 2026
DA  - 2026/05/28
TI  - Identifying MCCF Vulnerabilities and Preparing Datasets for Effective Analysis: A Comprehensive Approach
BT  - Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)
PB  - Atlantis Press
SP  - 264
EP  - 279
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-678-4_21
DO  - 10.2991/978-94-6239-678-4_21
ID  - Soni2026
ER  -