International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 185 - Issue 15 |
Published: Jun 2023 |
Authors: Kanishkar Indira, Kiruthi Thaker |
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Kanishkar Indira, Kiruthi Thaker . Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey. International Journal of Computer Applications. 185, 15 (Jun 2023), 59-64. DOI=10.5120/ijca2023922852
@article{ 10.5120/ijca2023922852, author = { Kanishkar Indira,Kiruthi Thaker }, title = { Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey }, journal = { International Journal of Computer Applications }, year = { 2023 }, volume = { 185 }, number = { 15 }, pages = { 59-64 }, doi = { 10.5120/ijca2023922852 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2023 %A Kanishkar Indira %A Kiruthi Thaker %T Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey%T %J International Journal of Computer Applications %V 185 %N 15 %P 59-64 %R 10.5120/ijca2023922852 %I Foundation of Computer Science (FCS), NY, USA
In the subject of recommendation engines, the cold start problem is a significant research topic. Due to a lack of knowledge about the user and/or services, the recommendation system is unable to predict the user's preferences or interested products, resulting in a cold start. Many people have sought to overcome the cold start problem in recommending generic domains such as music, movies, E-Commerce, and travel websites using different types of machine learning models. This work provides a survey of the most recent to the traditional methods used for solving the cold start problem and also provides a holistic view of the adversarial attacks that are possible on the machine learning models used while trying to solve the cold start problem using the machine learning models.