MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202621093493 A) filed by Shahu Praful Gudadhe; and Sanjay Keshaorao Katait on August 01, 2026, for Ai-Driven Personalized Product Recommendation System For E-Commerce Platforms Using Adaptive User Preference Analysis.
Inventors include Shahu Praful Gudadhe; and Sanjay Keshaorao Katait.
The application for the patent was published on October 02, 2026, under issue no. 40/2026.
Abstract: ABSTRACT An AI-driven personalized product recommendation system for e-commerce platforms is disclosed. The system includes a user interaction module, data collection module, dynamic profile generator, product information processor, artificial intelligence engine, candidate product generator, recommendation ranking module, feedback analyzer, database, and administrative dashboard. Authorized user data comprising searches, clicks, viewing duration, cart actions, purchases, ratings, returns, wish lists, and contextual information is processed to determine customer preferences and current purchasing intent. Product descriptions, images, specifications, prices, availability, reviews, and category relationships are converted into machine-readable representations. Collaborative filtering, content-based filtering, contextual modeling, graph analysis, clustering, neural networks, or hybrid techniques calculate relevance scores between users and candidate products. The ranking module orders available products according to relevance, affordability, diversity, novelty, delivery feasibility, prior interactions, and configurable constraints. User responses to displayed recommendations generate feedback signals for updating profiles and recommendation models. Cold-start processing provides suggestions for new users and products using session behavior, declared preferences, metadata, similarity, and segment trends. Privacy controls provide consent management, anonymization, encryption, access restriction, and retention control. The system thereby improves product discovery, recommendation accuracy, customer engagement, conversion probability, satisfaction, and efficiency across websites, mobile applications, marketplaces, and omnichannel retail environments.
Disclaimer: Curated by HT Syndication.
