Book Recommendation System Using Style Values and Experiential Language Tags
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Solution Overview
Problem
Current publishing pipelines face challenges in effectively recommending books to readers based on their preferences, as existing methods often rely on generic genre and purchase history, failing to provide tailored recommendations that match readers' distinct writing style and experiential preferences.
Innovation Solution
A method and system that analyze book content and reader reviews to generate style values and experiential language tags (ELTs), creating a prediction model to forecast reader experiences with new books, allowing for personalized recommendations based on writing style and reader experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic genre and purchase history are used for book recommendations, then the recommendation system is simple to implement, but the recommendation accuracy and personalization fail to meet reader preferences
Solution Approach 1:
The patent segments book recommendations into multiple dimensions: genre classification, writing style analysis (vocabulary, sentence structure, narrative techniques), and reader experience prediction. This segmentation allows the system to analyze and recommend books based on specific aspects rather than treating all books uniformly, thereby improving recommendation accuracy while managing complexity through modular analysis components.
Solution Approach 2:
The patent transforms book characteristics from simple genre labels into multiple measurable parameters including style values (vocabulary complexity, sentence structure, narrative techniques) and predicted reader experiences (engagement level, emotional impact, readability). These parameter changes enable more precise matching between reader preferences and book characteristics, significantly improving recommendation accuracy.
2Adaptability or versatility
If style values and experiential language tags are generated for all books, then personalized recommendations improve, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis by pre-computing style values and experiential language tags for books in the database before they are needed for recommendations. This preliminary action stores extracted features (vocabulary complexity, sentence structure, narrative techniques) and predicted reader experiences in advance, so that when a recommendation is needed, the system only needs to retrieve and compare pre-computed data rather than analyzing entire books in real-time, significantly reducing processing time.
Solution Approach 2:
The patent creates simplified representations (copies) of books in the form of style values and experiential language tags that capture essential characteristics without requiring the full book text. These copied features serve as proxies for actual books during recommendation processing, enabling fast comparison and matching while preserving the ability to provide personalized recommendations based on detailed stylistic and experiential attributes.
3Measurement precision
If the ELT prediction model is trained on comprehensive book data, then prediction accuracy improves, but the model training complexity and data processing requirements increase
Solution Approach 1:
The patent extracts specific relevant features from comprehensive book data, focusing on style values (vocabulary complexity, sentence structure, narrative techniques) and reader experience indicators rather than using all available book attributes. This extraction process isolates the most predictive features for reader experience, improving prediction accuracy while reducing model training complexity by eliminating irrelevant or redundant data elements.
Solution Approach 2:
The patent transforms book characteristics into a different dimensional space by converting raw text and metadata into style values and experiential language tags that exist in a standardized feature space. This dimensionality change organizes diverse book attributes into consistent numerical representations that are more suitable for machine learning models, improving prediction accuracy while simplifying the training process through uniform data structures.
Data Source
AI summary
A method includes generating style values and experiential language tags (ELTs) for a plurality of books based on retrieved book content and reader reviews, respectively. The method further includes generating an ELT prediction model based on the style values and the ELTs. The ELT prediction model is configured to receive a set of style values for a new book and output a set of predicted ELTs for the new book, the set of predicted ELTs indicating predicted reader experiences with the new book. The method further includes receiving user-submitted book content from a remote user device, determining style values for the user-submitted book content, and determining a list of predicted ELTs for the user-submitted book content using the style values for the user-submitted book content and the ELT prediction model. Additionally, the method includes transmitting, to the user device, the list of predicted ELTs for the user-submitted book content.