A system for personalized product recommendations using AI / ML APIs in an e-commerce platform

Abstract

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

Description

[0001] The present invention relates to artificial intelligence and machine learning (AI / ML) based recommendation systems, in particular to a system for providing personalized product recommendations in an e-commerce platform using AI / ML APIs.

[0002] E-commerce platforms have revolutionized the shopping experience by providing customers with a vast selection of products. However, as product catalogs become increasingly extensive, users often struggle to find relevant items that match their preferences. To address this problem, recommender systems have been integrated into e-commerce platforms to increase customer engagement and boost sales. These systems aim to deliver personalized product suggestions based on user interactions, browsing history, and purchasing behavior. Despite their advantages, traditional recommender systems suffer from several limitations that impact their accuracy, adaptability, and overall effectiveness.

[0003] Traditional recommendation approaches can be broadly divided into rule-based filtering, collaborative filtering, and content-based filtering. Rule-based filtering systems rely on predefined conditions, such as "customers who bought X also bought Y," which limits their ability to dynamically adapt to individual preferences. Collaborative filtering, widely used in many e-commerce platforms, identifies patterns in user interactions and recommends products based on similarities between users. However, due to the "cold start problem," in which insufficient data about a user's preferences leads to poor recommendation quality, it often fails to provide accurate recommendations for new users. Furthermore, collaborative filtering struggles with scalability when dealing with large data sets, resulting in increased computational costs and slower response times.

[0004] Content-based filtering, on the other hand, analyzes product attributes and matches them with user preferences. While this method can generate relevant recommendations based on product descriptions, it has a limited scope and cannot suggest items that are not part of the user's previous selection, making it less effective at introducing new and diverse products. Furthermore, traditional recommendation systems are unable to adapt in real time to dynamic user behavior, seasonal trends, and contextual factors such as location, device type, and time of day. As a result, users often receive outdated or irrelevant recommendations that are not aligned with their changing preferences.

[0005] Another major disadvantage of traditional systems is the inability to incorporate real-time feedback for continuous improvement. Many recommender systems rely on static models that are updated at regular intervals, rather than dynamically adapting recommendations based on real-time interactions. This results in a delay in adapting to changing user behavior and reduces overall engagement. Furthermore, security and privacy concerns pose a significant challenge, as traditional systems often struggle to process and analyze large amounts of user data while maintaining compliance with privacy regulations.

[0006] To solve this problem, the present invention provides a system for personalized product recommendations using AI / ML APIs in an e-commerce platform.

[0007] The personalized product recommendation system that uses AI / ML APIs in an e-commerce platform can analyze user behavior, browsing habits, purchase history, and preferences to make tailored suggestions.

[0008] The personalized product recommendation system using AI / ML APIs in an e-commerce platform can enable real-time adaptation to dynamic user interactions and ensure that product recommendations are continuously refined based on recent user actions, contextual factors (such as location, time of day, and device type), and market trends.

[0009] The system for personalized product recommendations using AI / ML APIs in an e-commerce platform can improve the diversity and relevance of recommendations by incorporating hybrid recommendation strategies that combine collaborative filtering, content-based filtering, and AI-driven predictive analytics.

[0010] The personalized product recommendation system that uses AI / ML APIs in an e-commerce platform can be seamlessly integrated into e-commerce platforms via an API-based architecture, enabling easy implementation, data exchange, and interoperability with various third-party applications such as CRM systems (Customer Relationship Management) and inventory management solutions.

[0011] The personalized product recommendation system that leverages AI / ML APIs in an e-commerce platform can handle large amounts of user data and product lists without compromising response time or accuracy.

[0012] The personalized product recommendation system that leverages AI / ML APIs in an e-commerce platform can incorporate real-time user feedback mechanisms that allow customers to rate recommendations, provide feedback, and refine their preferences, enabling continuous learning and optimization of the system.

[0013] The personalized product recommendation system using AI / ML APIs in an e-commerce platform can ensure data privacy and security by implementing robust encryption and anonymization techniques and complying with global data protection regulations such as GDPR and CCPA.

[0014] The personalized product recommendation system using AI / ML APIs in an e-commerce platform can increase user engagement and conversion rates by providing contextual, highly relevant recommendations to increase customer satisfaction and drive sales on e-commerce platforms.

[0015] In one embodiment, a personalized product recommendation system using AI / ML APIs is deployed in an e-commerce platform. The system is designed to analyze massive amounts of user and product data, detect behavioral patterns, and generate accurate product recommendations in real time using advanced AI / ML techniques. It uses a modular AI / ML framework that seamlessly integrates with e-commerce platforms via API-based deployment, enabling real-time adaptation and continuous learning to dynamically refine recommendations. The system collects and processes user data, including browsing history, purchase records, search queries, demographic data, and contextual information such as location, device type, and session duration.It applies data cleaning, feature extraction, and natural language processing (NLP) techniques to structure the data for AI / ML processing. In parallel, product information is enriched using NLP and image recognition algorithms, extracting key attributes from product descriptions, images, and customer reviews to increase recommendation accuracy. The AI / ML-based recommendation engine uses a hybrid approach that combines collaborative filtering, content-based filtering, deep learning models, and contextual filtering to generate personalized suggestions. The system assigns probabilistic confidence scores to recommendations and continuously refines them based on implicit (clicks, dwell time) and explicit (ratings, feedback) user interactions.The system integrates reinforcement learning techniques, allowing it to learn from user interaction in real time and adjust recommendations accordingly. Context-aware filtering further increases relevance by incorporating dynamic elements such as seasonal trends, market fluctuations, and changes in user behavior. The system is designed for scalability and computational efficiency, ensuring seamless operation in large data sets and high-traffic e-commerce environments. API-based deployment enables easy integration with third-party applications such as customer relationship management (CRM) systems, inventory management solutions, and analytics tools. Furthermore, robust privacy and security measures are implemented, including encryption, anonymization, and compliance with global data protection regulations such as GDPR and CCPA.

[0016] The invention is explained again below with reference to the figure. It shows: Fig. : a system for personalized product recommendations using AI / ML APIs in an e-commerce platform.

[0017] Fig.shows a system for personalized product recommendations using AI / ML APIs in an e-commerce platform. The system for personalized product recommendations in an e-commerce platform includes a user data collection module, a data preprocessing module, a product information management module, a recommendation engine using machine learning models, a contextual filtering module, a feedback learning module, and an API integration module. The user data collection module is configured to collect browsing history, purchase records, search queries, and demographic information. The data preprocessing module is configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data.The product information management module is configured to extract, classify, and update product attributes using NLP and image recognition. The recommendation engine leverages machine learning models. The recommendation engine incorporates collaborative filtering, content-based filtering, and deep learning techniques to generate personalized product recommendations. The contextual filtering module is configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends. The feedback learning module is configured to continuously update recommendation models based on user ratings, reviews, and engagement metrics. The API integration module is configured to enable seamless connectivity with e-commerce applications, inventory management systems, and third-party analytics tools.The recommendation engine uses a hybrid approach that combines collaborative filtering, content-based filtering, deep learning models, and reinforcement learning to improve recommendation accuracy. The contextual filtering module dynamically adjusts recommendations by considering temporal factors, seasonal trends, user browsing behavior, and price fluctuations. The feedback learning module collects and analyzes implicit user feedback, including click-through rates, dwell time, and cart abandonment behavior, to improve the relevance of recommendations. The recommendation engine assigns a confidence score to each suggested product and ranks recommendations according to predicted user interest. The API integration module provides real-time access to recommendation updates and ensures compatibility with web, mobile, and smart assistant platforms.The product information management module continuously updates product attributes using AI-driven sentiment analysis from customer reviews. The privacy and security module implements data encryption, anonymization techniques, and compliance with GDPR and CCPA regulations to ensure secure handling of user data. The recommendation engine applies graph-based deep learning techniques to identify hidden user-product relationships for improved personalization. The system architecture is scalable to handle large volumes of user activity logs, product catalogs, and real-time data streams, ensuring efficient operation in high-traffic e-commerce environments. List of reference symbols 100 systems

Claims

[1] A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools. [2] The system of claim 1, wherein the recommendation engine uses a hybrid approach that combines collaborative filtering, content-based filtering, deep learning models, and reinforcement learning to improve recommendation accuracy. [3] The system of claim 1, wherein the context-dependent filtering module dynamically adapts the recommendations by taking into account temporal factors, seasonal trends, user browsing patterns, and price fluctuations. [4] The system of claim 1, wherein the feedback learning module collects and analyzes implicit user feedback, including click rates, dwell time, and shopping cart abandonment behavior, to improve the relevance of the recommendations. [5] The system of claim 1, wherein the recommendation engine assigns a confidence score to each suggested product and ranks the recommendations based on predicted user interest. [6] The system of claim 1, wherein the API integration module provides real-time access to recommendation updates and ensures compatibility with web, mobile, and smart assistant platforms. [7] The system of claim 1, wherein the product information management module continuously updates the product attributes using AI-driven sentiment analysis from customer reviews. [8] The system of claim 1, further comprising a privacy and security module that implements data encryption, anonymization techniques, and compliance with GDPR and CCPA regulations to ensure secure handling of user data. [9] The system of claim 1, wherein the recommendation engine applies graph-based deep learning techniques to identify hidden user-product relationships for improved personalization. [10] The system of claim 1, wherein the system architecture is scalable to process large volumes of user activity logs, product catalogs, and real-time data streams, thereby ensuring efficient operation in high-traffic e-commerce environments.

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