Natural language processing for personalized recommendation systems

Abstract

A system for generating personalized recommendations using natural language processing (NLP) and machine learning, which includes the following: a) a data collection module (101) configured to collect structured and unstructured data from multiple sources, including user profiles, past interactions, text inputs and contextual data; b) a Natural Language Understanding (NLU) module (102) that uses transformer-based NLP models to analyze unstructured data and extract features such as entities, moods, intentions and context factors; c) a personalization engine (103) configured to generate and update dynamic user embeddings representing user preferences, behaviors and contextual attributes, using transfer learning for real-time adaptability; d) a recommendation generator (104) that integrates collaborative filtering, content-based filtering and reinforcement learning algorithms to generate context-related and user-specific recommendations; e) a feedback loop module (105) configured to capture user interactions and feedback in order to iteratively retrain and improve the recommendation model; and f) a secure storage and processing unit (106) for managing data access and ensuring compliance with data protection regulations.

Description

[0001] Field of the invention: The present invention relates to the field of artificial intelligence and machine learning, in particular to systems that use natural language processing (NLP) to generate personalized recommendations based on user data, preferences, and context inputs.

[0002] Background of the invention: The rapid growth of digital platforms has led to an exponential increase in the availability of information and choices, making it difficult for users to identify relevant content. Traditional recommendation systems rely primarily on collaborative filtering or content-based filtering, which, while effective, often fail to account for the nuances of user preferences expressed in natural language.

[0003] Natural language processing offers the opportunity to close this gap by extracting insights from unstructured data such as user reviews, search queries, and social media interactions. However, existing NLP-based recommendation systems often have limited ability to understand context, sentiment, and intent, resulting in less accurate recommendations.

[0004] Another limitation of traditional systems is their inability to quickly adapt to changing user preferences and dynamic contexts. For example, a user's immediate interests can change significantly due to situational factors such as time of day, location, or current events. Existing systems lack the real-time adaptability required to provide truly relevant recommendations in such scenarios. Furthermore, many systems struggle with the cold start problem, where recommendations for new users or items are suboptimal due to a lack of historical data.

[0005] With advances in NLP, models such as transformers and neural embeddings have demonstrated remarkable success in capturing complex patterns in text data. These techniques, combined with reinforcement learning and transfer learning, offer the potential to revolutionize recommendation systems by enabling a deeper understanding of user behavior and intent. Furthermore, the integration of context awareness and continuous feedback mechanisms ensures that recommendations evolve with the user's needs, thus increasing overall satisfaction.

[0006] The present invention aims to address these challenges by introducing a comprehensive system that integrates advanced NLP techniques with machine learning models to deliver highly personalized and context-dependent recommendations.

[0007] Summary of the Invention: The invention provides a system for generating personalized recommendations by using NLP to analyze user-generated content, contextual data, and external data sources. The system uses a multi-layered architecture that includes: 1. Data collection module: Aggregates structured and unstructured data, such as user profiles, past interactions, and text input. 2. Natural Language Understanding (NLU): Processes and analyzes text data to extract entities, sentiment, intent, and context. 3. Personalization Engine: Uses user-specific embeddings generated from NLP models to customize recommendations. 4. Recommendation Generator: Combines contextual insights with machine learning algorithms to generate dynamic and relevant recommendations. 5. Feedback loop: Continuously refines the recommendation model based on user interactions and feedback.

[0008] This invention goes beyond conventional recommendation systems by combining the power of advanced NLP techniques with real-time adaptability. The data collection module ensures comprehensive data aggregation from various sources, enabling a holistic view of user preferences. The NLU component excels at processing unstructured data to gain meaningful insights, while the personalization engine creates dynamic user profiles to adapt to changing preferences in real time.

[0009] The Recommendation Generator uses collaborative and content-based filtering techniques enhanced by reinforcement learning to deliver not only accurate but also engaging suggestions. The feedback loop creates a self-improving system by continuously learning from user interactions, ensuring recommendations remain relevant and timely.

[0010] By integrating these components, the system achieves significant benefits in terms of precision, adaptability, and user satisfaction. It addresses key challenges such as understanding nuanced user behavior, adapting to dynamic contexts, and resolving cold-start issues. Furthermore, it includes metrics for diversity and serendipity that increase user engagement through surprising yet relevant suggestions. Short description of the drawing Fig.: shows a block diagram of the system according to the invention. Detailed description of the invention

[0011] The present invention presents a novel system that combines the power of natural language processing with advanced machine learning techniques to deliver personalized recommendations. The system's architecture was designed to overcome the limitations of existing recommendation systems by integrating multiple components that work together to increase user satisfaction and engagement.

[0012] The core component of the invention is the data collection module, which collects data from various sources, including user profiles, historical interactions, search queries, and unstructured text such as reviews and social media posts. This module ensures that the system has access to a comprehensive dataset, allowing it to create detailed user profiles. The data is stored in a secure and privacy-compliant central repository, enabling efficient access and processing.

[0013] The Natural Language Understanding (NLU) component leverages cutting-edge NLP models, such as Transformer architectures (e.g., BERT, GPT), to process text input. This component performs tasks such as tokenization, named entity recognition, sentiment analysis, and intent recognition. For example, if a user searches for a specific product, the NLU module can extract keywords, understand the sentiment behind the query, and identify contextual factors that influence the user's preferences. By capturing these intricate details, the system is able to generate more precise and contextually relevant recommendations.

[0014] The personalization engine is the heart of the system, tailoring recommendations to the individual user. It creates user embeddings—mathematical representations of a user's preferences, behaviors, and contextual attributes. These embeddings are updated in real time using transfer learning techniques, allowing the system to dynamically adapt as new data becomes available. For example, if a user suddenly starts browsing fitness-related content, the system can adjust its recommendations to include health and wellness products or services.

[0015] The recommendation generator combines insights gained from the personalization engine with collaborative and content-based filtering techniques. This component uses reinforcement learning algorithms to optimize the recommendation process over time. Furthermore, the generator considers diversity and serendipity metrics to ensure that recommendations not only match user preferences but also offer new and interesting options. This balance prevents user fatigue and ensures a fresh and engaging experience.

[0016] The system's feedback loop plays a critical role in maintaining and improving its performance. It tracks user interactions with recommendations, such as clicks, shares, purchases, and ratings. This feedback is used to continuously retrain the machine learning models, ensuring the system remains relevant and accurate as user preferences change. For example, if a user consistently ignores certain types of recommendations, the system learns to give those suggestions less priority in the future.

[0017] The modular design of the invention allows for use in a wide range of areas, including e-commerce, media streaming, education, and healthcare. In e-commerce, for example, the system can recommend products based on a combination of past purchases, current search queries, and seasonal trends. In healthcare, it can suggest personalized wellness plans by analyzing medical history and current health data.

[0018] The integration of advanced NLP techniques with machine learning algorithms makes this system highly scalable and adaptable. It can handle large data sets and diverse user groups, ensuring its applicability in both small and enterprise environments. Furthermore, its real-time processing capabilities make the system suitable for dynamic and rapidly changing scenarios where user preferences can shift rapidly.

[0019] The invention operates as a unified system that leverages natural language processing (NLP) and machine learning to dynamically generate personalized recommendations. The system's functionality can be illustrated using the following steps and a practical example.

[0020] The system begins by aggregating data from various sources, including user profiles, interaction history, unstructured data (e.g., product reviews, social media posts), and real-time contextual information. For example, consider a user, Alice, browsing an e-commerce platform for fitness products. The system collects Alice's past purchases, browsing history (e.g., yoga mats, running shoes), and recent searches like "best fitness trackers for women."

[0021] In addition, the system considers contextual data such as Alice's location, time of day, and seasonal trends (e.g., increased interest in fitness equipment during New Year's resolutions). This raw data is preprocessed to ensure consistency and eliminate noise.

[0022] The NLU component analyzes the text data associated with Alice's activities. Using cutting-edge NLP models such as Transformers, the system processes Alice's search query "best fitness trackers for women." The model identifies keywords ("fitness tracker," "women"), extracts sentiment (e.g., positive purchase intent), and detects contextual elements (e.g., fitness focus).

[0023] In addition, the system examines reviews and descriptions of fitness trackers Alice has interacted with and understands terms like "waterproof," "pedometer," or "calorie counter." Thanks to this detailed understanding, the system can make recommendations tailored to Alice's preferences.

[0024] The personalization engine constructs a dynamic user embedding for Alice that represents her preferences in a multidimensional vector space. This embedding captures her interest in fitness equipment, gender-specific products, and specific features such as waterproofing. The embedding is updated in real time as Alice continues to interact with the platform, ensuring that recommendations evolve with her behavior.

[0025] For example, if Alice suddenly starts reading articles about marathon training, the embedding adapts to favor products such as advanced running shoes or GPS-enabled fitness trackers.

[0026] The recommendation generator combines Alice's embedding with collaborative filtering (e.g., products purchased by users with similar interests) and content-based filtering (e.g., products with similar attributes to their previous purchases). The system uses reinforcement learning to optimize recommendations, taking into account diversity and serendipity metrics.

[0027] As a result, the system recommends a mix of familiar and new products, such as a waterproof fitness tracker with a built-in heart rate monitor and a new brand of lightweight running shoes. The recommendations are presented visually on Alice's screen and accompanied by relevant contextual information, such as a special discount on fitness trackers.

[0028] Alice interacts with the recommendations by clicking on the fitness tracker, reading its description, and adding it to her shopping cart. These actions provide implicit feedback that the system uses to refine its models. For example, the system learns that Alice values ​​heart rate monitoring and prioritizes similar features in future recommendations. If Alice rates the product positively after purchase, this feedback strengthens the accuracy of the recommendation algorithm.

[0029] Through this process, Alice experiences a seamless and personalized shopping experience. She discovers products that precisely match her needs, including unexpected but appealing items like a discounted running shoe for her marathon training. The system adapts dynamically, ensuring that Alice's changing preferences are always met.

[0030] This example illustrates the system's ability to deliver contextual, precise, and engaging recommendations that significantly enhance the user experience. Beyond e-commerce, the same workflow also applies to areas such as media streaming (e.g., recommending movies based on viewing history and sentiment analysis of user reviews) or healthcare (e.g., suggesting wellness plans based on medical history and current condition). This adaptability underscores the broad applicability and impact of the invention.

[0031] In summary, the present invention provides a comprehensive solution for generating personalized recommendations by leveraging the power of natural language processing and advanced machine learning techniques. The innovative architecture overcomes the limitations of conventional systems and offers significant improvements in accuracy, adaptability, and user satisfaction. Description of the numbers in the attached drawing 100 systems 101 Data acquisition module 102 Natural Language Understanding (NLU) Module 103 Personalization Machine 104 Recommendation Generator 105 Feedback module 106 secure storage and processing unit

Claims

[1] A system for generating personalized recommendations using natural language processing (NLP) and machine learning, comprising: a) a data collection module (101) configured to collect structured and unstructured data from multiple sources, including user profiles, past interactions, text inputs, and contextual data; b) a Natural Language Understanding (NLU) module (102) that uses transformer-based NLP models to analyze unstructured data and extract features such as entities, sentiments, intents, and contextual factors; c) a personalization engine (103) configured to generate and update dynamic user embeddings representing user preferences, behaviors, and contextual attributes, using transfer learning for real-time adaptability; d) a recommendation generator (104) that integrates collaborative filtering, content-based filtering and reinforcement learning algorithms to generate contextual and user-specific recommendations; e) a feedback loop module (105) configured to capture user interactions and feedback to iteratively retrain and improve the recommendation model; and (f) a secure storage and processing unit (106) to manage data access and ensure compliance with data protection regulations. [2] The system of claim 1, wherein the NLU module uses pre-trained transformation models, such as BERT or GPT, to perform tokenization, named entity recognition, sentiment analysis, and intent recognition on text data. [3] The system of claim 1, wherein the personalization engine uses a hybrid embedding framework that combines contextual and behavioral data to create multidimensional user profiles that are dynamically updated based on user activity. [4] The system of claim 1, wherein the recommendation generator incorporates diversity and serendipity metrics to increase user engagement by suggesting a combination of familiar and new content. [5] The system of claim 1, wherein the feedback loop module uses both explicit feedback, such as user ratings and reviews, and implicit feedback, such as clicks and dwell time, to refine the recommendation algorithm and adapt to evolving user preferences.

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