AI-DRIVEN PERSONALIZED RECOMMENDER SYSTEM

Abstract

The present invention relates to an AI-driven personalized recommender system that leverages machine learning, deep learning, and contextual information to deliver highly relevant and dynamic recommendations to users in real-time. By integrating both explicit user preferences and implicit behavioral data, along with contextual factors such as time, location, and device, the system continuously adapts to evolving user needs and preferences. Utilizing advanced algorithms like neural networks, reinforcement learning, and contextual bandit models, the system enhances user satisfaction by providing personalized recommendations that reflect both long-term interests and short-term behavior, ensuring a more engaging and tailored user experience.

Specification

Description:The embodiments of the present invention generally relates to the field of personalized recommendation systems, and more particularly, to an AI-driven recommender system that utilizes machine learning, deep learning, and contextual information to provide dynamic, personalized recommendations based on individual user preferences and behaviors in real-time.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Recommender systems have become integral to a wide range of applications, including e-commerce platforms, online content streaming, and social media services. Traditional approaches in recommender systems , Claims:1. A personalized recommendation system, comprising:
a data collection module configured to gather explicit user preferences and implicit behavior data, including at least one of user ratings, clicks, browsing history, and purchasing behavior;
an AI-driven recommendation engine utilizing machine learning models, including neural networks and reinforcement learning, to analyze the collected data and generate personalized recommendations;
a contextual awareness layer integrated into the recommendation engine to incorporate contextual information, including at least one of time of day, user location, device type, and environmental conditions, to refine the recommendations;
a real-time feedback loop that continuously updates the recommendation model based on user interactions, ensuring dynamic adaptation of the system to changing user preferences.

2. The system of claim 1, wherein the data collection module further incorporates social media interactions and user-generated content, including likes, shares, and co

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