Product Recommender System: OpenAI Text Embedding

About this course

A renowned online shopping platform named GlimmerGate has hired you, as an AI Engineer, to help them improve their product recommendation system. They aim to provide personalized recommendations to users based on their recent product views. They have provided a product dataset containing information such as title, description, and ID, about 2000 of their products. Additionally, they have supplied a list of 10 recently viewed products by a user. They want you to develop a prototype to recommend products that the user has never viewed before, based on their recently viewed products. As an AI Engineer, your responsibility is to leverage OpenAI's text embedding models to develop a text-based recommendation system using Python. By analyzing the text embeddings of the viewed products and comparing them with the entire product database, your system will generate recommendations that align with the user's preferences. This prototype will significantly enhance the platform's user experience by offering relevant and engaging product suggestions on the GlimmerGate website's main page, ultimately boosting customer satisfaction and retention rates. To get the most out of this course, you'll need access to the OpenAI API Key and a basic understanding of data analysis concepts, including data types, and data manipulation, along with some familiarity with Python. This course is for those who are experienced data analysts with at least a basic knowledge of Python and want to explore the exciting applications of generative AI in data analysis.

Course objectives

• create a functional prototype for product recommendations
• enhance user experience through personalized suggestions
• boost customer satisfaction and retention rates