AI-Powered Product Recommendation And Purchase Probability Analysis

This article presents an AI-powered recommendation system specifically created for e-commerce platforms, targeting improvements in product discovery and personalization. This recommendation system merges a thirdparty application programming interface (API) with an inhouse recommendation engine to evaluate requests made by a consumer, allowing the system to suggest products that are currently available for sale. The hybrid recommendation strategy integrates combined models such as collaborative filtering and content-based filtering to improve accuracy. A key component of the recommendation system is the purchase probability prediction module, which enables supervised learning algorithms, such as Gradient Boosting and Random Forest classifiers, to predict purchases based on previous consumer purchase activity, such as what was "added to cart" "added to wishlist" or "purchased in the past" by an individual. In addition, the recommendation system includes models based on natural language processing (NLP) that summarize every product, using particular structures that characterize the recommendations into Best Value, Premium, and Budget suggestions for products, and comparative summaries for every product recommendation. Results show an increase in conversion of 27% and an increase in average order value of 19% versus standard product recommendation algorithms. This system has multiple factors for recommending products based on purchase predictions provided from machine learning models and utilizes NLP to summarize and categorize products with a strong advance in personalized experiences in ecommerce