Autogenie: Revolutionizing Vehicle Recommendations with Hybrid ML-Based System

In an ever-changing car market, it is still difficult to choose the best car from a wide range of options. "AUTOGENIE" offers an innovative solution to this problem combining matrix factorization, collaborative filtering, and content filtering in a hybrid machine learning system In this varied environment, traditional recommendation methods have problems in making recommendations true and best to give in. When a particular algorithm is used, the recommendations it makes fail in some way–whether accuracy, precision, or personal preference–that upsets the user's AUTOGENIE filling this void provides through complementary methods, and improves the accuracy of recommendations by analyzing large data sets and identifying hidden variables Furthermore, this hybrid machine learning algorithm is also able to adapt to simple data and changes the user Preferences Using advances in deep metric multiplication for cross-domain recommendation, AUTOGENIE works exceptionally well for customizing recommendations despite changing user preferences and limited data It uses the information provided are personalized recommendations, so they reflect recommendations that are highly relevant to them. This paper describes the methodology of AUTOGENIE, including its new methodology for studying hybrid devices, experimental results and implications. By combining collaborative filtering, matrix factorization, and content filtering, AUTOGENIE provides a convenient way forward for highly customized vehicle recommendations in the dynamic automotive industry in.