ADAPTIVE SYSTEM FOR REAL-TIME NOISE REDUCTION IN WEB DATA USING DYNAMIC USER INTEREST PROFILING

Abstract

7. ABSTRACT The present invention provides a dynamic noise reduction system for web data that adapts to users' evolving interests. Utilizing advanced machine learning algorithms, the system reduces irrelevant content by continuously analyzing user behavior such as clicks, dwell time, and browsing patterns—to create a personalized user profile. A Content Analysis Module processes incoming web data, assigning relevance scores based on alignment with the user’s profile, recency, source reliability, and content novelty. A Noise Reduction Engine filters out low-relevance content in real time, while an Adaptive Filtering Mechanism adjusts parameters based on situational factors like location and device type. Additionally, a User Feedback Loop enables users to provide direct input on content relevance, refining the system’s accuracy over time. This approach ensures a streamlined browsing experience, presenting users with only the most relevant and engaging content while minimizing distractions from unwanted information. The figure associated with abstract is Fig. 1.

Specification

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to computer science and data engineering, specifically to dynamic noise reduction in web data. It employs machine learning algorithms to filter irrelevant information, adapting in real-time to changes in user interests for a more personalized browsing experience.

Background of the Invention

With the rapid growth of digital content, users frequently face an overwhelming amount of irrelevant information, or "noise," when browsing the web. Traditional filtering methods, like keyword-based or rule-based filters, help reduce noise but are limited. These methods often use fixed rules and cannot adapt to users' changing preferences over time, making them inefficient for personalizing web content. Collaborative filtering, another common approach, also lacks effectiveness when user interests shift frequently.

Recent advancements in machine learning and artificial intelligence have led to more adaptive systems that can learn from user behavior. These systems can adjust content relevance based on real-time user interactions, offering a more customized browsing experience. However, existing adaptive systems face challenges with accuracy, speed, and privacy. Processing large volumes of data in real time, maintaining user privacy, and accurately distinguishing valuable content from noise are critical hurdles.

This invention introduces a dynamic system for noise reduction in web data, powered by advanced machine learning algorithms that continuously learn and adapt to user interests. The system builds a user profile based on interactions like clicks and time spent on content. It scores incoming content for relevance and filters out low-relevance data in real time. Additionally, it includes a feedback loop, where users can mark content as relevant or irrelevant, allowing the system to refine its accuracy over time.

This approach ensures that users receive content tailored to their evolving interests, providing a more relevant and streamlined browsing experience, minimizing irrelevant information, and enhancing engagement.

Brief Summary of the Invention

The primary objective of the present invention is to develop a system that can adapt to the user's changing interests dynamically. The machine learning algorithms used in the system ensure that it continuously refines its understanding of user preferences, providing more relevant content as user behavior and needs evolve.

Another objective of the invention is to improve the user experience by minimizing exposure to irrelevant information. By reducing web noise, the system offers users a cleaner, more focused browsing environment, allowing them to find relevant information more efficiently and with less effort.

A further objective is to deliver real-time content filtering that operates with high efficiency. This objective is crucial for handling large volumes of data quickly, enabling the system to provide immediate, contextually relevant recommendations without lag, thus enhancing both usability and responsiveness.

In today's digital world, users face a growing challenge in managing the vast volumes of information available on the internet. While this abundance has transformed the web into a powerful tool for knowledge and resource-sharing, it has also led to a significant problem: information overload. Users often encounter irrelevant data, or "noise," which hinders their ability to efficiently access the information they seek. To address this issue, the present invention introduces a dynamic noise reduction system for web data that tailors content specifically to each user's preferences and needs. This system uses advanced machine learning algorithms to filter out noise by continuously learning and adapting to users' evolving interests, delivering a more streamlined and engaging browsing experience.

The core of this invention is a multi-module system that builds and refines a user profile based on behavioral data such as click patterns, page dwell time, and browsing history. Unlike traditional filtering methods, which often rely on fixed parameters or static rules, this invention offers an adaptive approach, capable of recognizing shifts in user interests over time. It ensures that only relevant content reaches the user, thus reducing distractions from irrelevant information and helping users focus on their areas of interest. The system operates continuously, updating user profiles in real time and adjusting the relevance of content accordingly.

A key component of this invention is its user interest profiling module, which tracks interactions such as time spent on pages, frequency of visits, and types of content accessed. This data is analyzed using machine learning algorithms that identify patterns in user behavior, allowing the system to construct a comprehensive and evolving profile for each user. By combining explicit feedback, like user ratings, with implicit feedback, like engagement levels, the profiling module ensures that the system adapts accurately to individual preferences. This real-time adaptability is essential for users whose interests may change frequently due to factors such as work needs, seasonal interests, or situational demands.

To complement this profiling, the invention includes a content analysis module that processes incoming web data and evaluates it against each user's profile. This module uses techniques like natural language processing (NLP) to understand the content's context, relevance, and topical alignment with the user's interests. By assigning a relevance score to each piece of content, the module effectively determines whether the content should be presented to the user or filtered out as noise. This scoring system is crucial for maintaining a balance between aggressive filtering and the preservation of valuable information, as it allows the system to refine its recommendations without sacrificing essential content.

The noise reduction engine then takes the relevance scores generated by the content analysis module and filters out low-relevance content in real time. The noise reduction process is highly customizable, allowing users to set their desired level of filtering sensitivity. For example, a user who prefers a high degree of filtering can choose to remove all content below a certain relevance threshold, while a user with broader interests may allow a wider range of content. This customization ensures that users can tailor the filtering process to match their preferences, creating a more flexible and user-centric experience.

An important aspect of this invention is the user feedback loop, which further enhances the system's precision. Users have the option to provide feedback on content, marking it as relevant or irrelevant, which is then fed back into the system to refine the noise reduction algorithms. Over time, this continuous feedback mechanism makes the system increasingly accurate, as it learns from user interactions and adjusts its filtering criteria. This feedback loop not only improves the relevance of content but also helps the system adapt to long-term changes in user behavior, making it highly responsive and personalized.

Moreover, the system incorporates contextual awareness, allowing it to adjust its filtering based on situational cues such as location, time of day, and device type. For instance, if a user is researching a specific topic, the system can temporarily prioritize related content, even if it typically falls outside their usual interests. This contextual adaptability ensures that the system remains relevant to the user's immediate needs, providing a seamless experience across various situations and use cases.

In summary, this invention represents a significant advancement in the field of noise reduction for web data. By combining machine learning, real-time adaptability, and user feedback, it provides a robust and effective solution to the problem of information overload. The system enhances user productivity and engagement by delivering a focused browsing experience, free from the distractions of irrelevant data. Furthermore, the incorporation of dynamic profiling, content scoring, customizable filtering, and contextual awareness sets this invention apart from traditional static filtering methods, making it a powerful tool for personalized content delivery.
Brief Summary of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Figure 1 illustrates the flowchart representing the dynamic noise reduction system, in accordance to an exemplary embodiment of the present invention.

Detailed Description of the Invention

The present disclosure emphasises that its application is not restricted to specific details of construction and component arrangement, as illustrated in the drawings. It is adaptable to various embodiments and implementations. The phraseology and terminology used should be regarded for descriptive purposes, not as limitations.

The terms "including," "comprising," or "having" and variations thereof are meant to encompass listed items and their equivalents, as well as additional items. The terms "a" and "an" do not denote quantity limitations but signify the presence of at least one of the referenced items. Terms like "first," "second," and "third" are used to distinguish elements without implying order, quantity, or importance.

According to the exemplary embodiment of the present invention a dynamic noise reduction system for web data, designed to enhance the user experience by filtering out irrelevant information and delivering personalized content. This system utilizes machine learning algorithms and continuous user profiling to adapt in real time to the user's changing interests and browsing context. Through a combination of adaptive filtering, real-time data processing, and user feedback integration, the invention creates a tailored, efficient, and distraction-free browsing experience.

In accordance with the exemplary embodiment of the present invention the key components include:

In accordance with the exemplary embodiment of the present invention the user interest profiling module is responsible for tracking and analyzing the user's interactions with online content to build a personalized interest profile. It observes user behaviors such as page clicks, time spent on specific pages, repeat visits, and interactions (likes, shares, etc.). These signals are used to create a dynamic, evolving user profile. The profiling module integrates advanced machine learning algorithms to analyze both explicit feedback (e.g., user ratings) and implicit feedback (e.g., dwell time on a page) for greater accuracy. The machine learning algorithms may include techniques like neural networks, decision trees, or clustering models to identify patterns in user behavior. This process allows the system to continually refine the user's profile, ensuring that it adapts to both short-term interests and long-term preferences as they evolve over time.

In accordance with the exemplary embodiment of the present invention the content analysis module processes incoming web data to evaluate its relevance based on keywords, topic sentiment, and overall context. This module applies natural language processing (NLP) techniques to detect topics, identify key terms, and assess the sentiment or tone of content. By analyzing the content contextually, the system can differentiate between relevant and irrelevant information based on the user's unique interests.

In accordance with the exemplary embodiment of the present invention each piece of content is assigned a relevance score that indicates its alignment with the user's profile. This score is calculated using various factors, including:
• Topical Relevance: Alignment with topics the user frequently interacts with.
• Recency: Prioritization of fresh, timely content.
• Source Reliability: Weight given to content from trusted or high-quality sources.
• Content Novelty: Emphasis on presenting unique or previously unseen content that might interest the user.
By leveraging these factors, the Content Analysis Module enables the system to distinguish between noise and valuable information effectively.

In accordance with the exemplary embodiment of the present invention the noise reduction engine utilizes the relevance scores generated by the Content Analysis Module to filter out low-relevance content dynamically. The engine's filtering thresholds can adjust automatically based on the user's current browsing context, preferences, and changes in interest over time. This engine operates with customizable filtering sensitivity, allowing users to set their preferred level of noise reduction. For instance, users may choose to maintain a highly focused browsing experience by setting a high threshold, allowing only the most relevant content, or they may opt for broader recommendations if they wish to explore diverse topics. This adaptability ensures that the system remains user-centric, balancing between aggressive noise reductions and preserving useful content.

In accordance with the exemplary embodiment of the present invention a continuous user feedback loop is integrated into the system to refine the filtering and profiling algorithms. Users can mark content as relevant or irrelevant, and this feedback directly influences the noise reduction algorithms, making the system progressively more accurate. This feedback is processed in real time, enabling the system to adapt quickly to changes in user preferences. The feedback loop is essential for fine-tuning the Content Analysis Module and the Noise Reduction Engine, helping the system achieve an optimal balance between filtering accuracy and content relevance. Over time, the system learns from the feedback, ensuring that it becomes increasingly effective in understanding and meeting the user's evolving needs.

In accordance with the exemplary embodiment of the present invention one of the unique features of this invention is its ability to adjust filtering criteria based on situational or contextual cues. The system can adapt based on factors such as location, time of day, device type, and user activity. For example, if the system detects that the user is researching a particular topic, it may temporarily prioritize related content and relax filters on unrelated topics. Similarly, if a user is browsing on a mobile device, the system may adjust its criteria to favor shorter, mobile-friendly content.

In accordance with the exemplary embodiment of the present invention the system operates in a cyclical and iterative process, which allows it to continuously learn from user behavior, refine filtering parameters, and adapt to the user's changing interests. The following steps outline the primary operational workflow:
Data Collection: The system begins by collecting data on user interactions with web content, such as page views, dwell time, click behavior, and explicit feedback.
Interest Profiling: Using machine learning algorithms, the system analyzes this data to develop a dynamic user interest profile. This profile is updated in real time, reflecting shifts in the user's short-term and long-term preferences.
Content Relevance Scoring: The system processes incoming web content, using NLP and other analytical techniques to assign relevance scores. These scores indicate how well the content aligns with the user's current profile.
Dynamic Filtering: Based on relevance scores and contextual cues, the system filters out low-relevance content. Filtering criteria are adjusted dynamically to maintain a balance between noise reduction and information preservation.
Feedback Integration: User feedback on the presented or filtered content is incorporated continuously, enabling the system to fine-tune its relevance assessment and noise filtering processes.

Referring to the Fig.1, the sequence of processes in a dynamic noise reduction system for web data. It begins with User Interaction Data Collection, which gathers user behavior information such as clicks, time spent on pages, and browsing patterns. This data flows into the User Interest Profiling Module, where user interests are identified and dynamically updated. Next, the Content Analysis Module evaluates incoming data, applying natural language processing to assess topic relevance and context. This analysis is followed by Relevance Scoring, where content is rated based on alignment with user interests. The Noise Reduction Engine then filters out content deemed irrelevant, minimizing distractions for the user. A User Feedback Loop continuously refines the system's understanding by incorporating user feedback on presented content. Additionally, an Adaptive Filtering Mechanism adjusts filtering thresholds based on situational factors like location and device type, with Contextual Data providing additional cues. Finally, Filtered & Relevant Content Display presents the user with a tailored, distraction-free browsing experience.

Benefits and Applications
The system significantly improves the browsing experience by reducing the cognitive load on users and providing a more focused environment. It is particularly useful in domains where content overload is common, such as news platforms, academic research databases, and social media. By dynamically adjusting to each user's unique interests and context, the system maximizes relevance, making information retrieval faster, more efficient, and tailored to individual preferences.

Additionally, this invention contributes to enhanced user profiles for improved personalization across other applications, including targeted advertising, recommendation engines, and content management systems. Its adaptable and user-centric design ensures it remains relevant even as user preferences and web content change, positioning it as a state-of-the-art solution for web data management and personalization. , C , Claims:5. CLAIMS
I/We Claim:
1. A system for dynamic noise reduction in web data based on user interest learning, comprising:
a. a user interest profiling module configured to track and analyze user interactions with web content, including page clicks, time spent on pages, and engagement metrics, to build and dynamically update a user profile that reflects evolving user preferences;
b. a content analysis module configured to process incoming web data, applying natural language processing (NLP) and other analytical techniques to assign a relevance score to each piece of content based on its alignment with the user profile, topic relevance, recency, source reliability, and content novelty;
c. a noise reduction engine configured to filter out content with low relevance scores in real time, wherein the Noise Reduction Engine dynamically adjusts filtering thresholds based on the user's current context and preferences; and
d. a user feedback loop allowing users to mark content as relevant or irrelevant, enabling the system to continuously refine relevance scoring and filtering parameters;
e. wherein the system dynamically reduces irrelevant content from web data by leveraging machine learning algorithms to adapt to the user's changing interests and context in real time.

2. The system as claimed in claim 1, wherein the User Interest Profiling Module incorporates machine learning algorithms, selected from the group comprising neural networks, decision trees, and clustering models, to analyse explicit feedback and implicit feedback from user interactions to refine the user profile continuously.

3. The system as claimed in claim 1, wherein the content analysis module assigns a relevance score to each content item based on contextual factors, including:
• topical relevance to align with user interests,
• content recency to prioritize recent information,
• source reliability based on credibility of content origin, and
• content novelty for identifying unique or previously unseen content.

4. The system as claimed in claim 1, wherein the noise reduction engine allows customizable filtering sensitivity, enabling users to select a desired noise reduction level by setting a filtering threshold according to personal preference.

5. The system as claimed in claim 1, further comprising an adaptive filtering mechanism configured to modify filtering parameters based on situational cues, including user location, time of day, device type, and user activity, thereby tailoring the relevance scoring and filtering criteria to the user's specific context.

6. The system as claimed in claim 1, wherein the user feedback loop incorporates real-time user feedback, allowing the system to dynamically update relevance scores and filtering thresholds for content based on recent user interactions.

7. The system as claimed in claim 1, wherein the content analysis module employs natural language processing to identify topic sentiment and context, enabling the system to recognize shifts in user interests and adjust content recommendations accordingly.

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