Abstract

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.

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