International Journal of Applied Information Systems |
Foundation of Computer Science (FCS), NY, USA |
Volume 12 - Number 44 |
Year of Publication: 2024 |
Authors: Kanishkar Indira, Kiruthi Thaker |
10.5120/ijca2023922852 |
Kanishkar Indira, Kiruthi Thaker . Data Security Concerns in Approaches to Overcome Cold Start Problem in Recommender Systems - A Survey. International Journal of Applied Information Systems. 12, 44 ( Jul 2024), 41-46. DOI=10.5120/ijca2023922852
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.