E-book Selection via Complete-Reading Probability and Time
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Solution Overview
Problem
Users face difficulties in selecting appropriate electronic books due to limited information on complete-reading probability and expected time, leading to inefficient browsing and potential mismatch with personal preferences.
Innovation Solution
A method and server system that provide user interfaces for retrieving electronic books by acquiring and displaying information on complete-reading probabilities and expected times, allowing users to set filters and queries for specific ranges, and sorting options based on these metrics, enabling more accurate selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic book information (cover, title, author, publisher, category, publication date) is provided, then the information structure remains simple, but users cannot accurately determine whether the book matches their preferences
Solution Approach 1:
The system pre-calculates complete-reading probability and expected time for complete reading for each electronic book based on historical reading data before users query. This preliminary preparation of predictive information allows users to make informed decisions without experiencing the contradiction during actual book selection
Solution Approach 2:
The system utilizes historical reading behavior data as feedback to continuously improve the accuracy of complete-reading probability predictions. By analyzing patterns from actual user reading completions, the system refines its predictive model to better match user preferences while maintaining information efficiency
2Measurement precision
If the number of views is provided for each electronic book, then popularity information is available, but it cannot accurately reflect complete-reading rates
Solution Approach 1:
The system introduces complete-reading probability as an intermediary metric that bridges the gap between simple view counts and detailed individual reading completion data. This intermediary measure synthesizes historical reading patterns into a single predictive value that accurately reflects complete-reading likelihood without exposing raw detailed data
Solution Approach 2:
The system transforms raw reading behavior data into a different parameter (complete-reading probability) that better represents the underlying phenomenon. By changing from counting views to calculating probability based on completion patterns, the system achieves more precise measurement of reading engagement
3Measurement precision
If total pages are provided to estimate reading time, then basic book metadata is available, but accurate expected time for complete reading cannot be determined
Solution Approach 1:
The system pre-calculates expected time for complete reading by analyzing historical reading time data before users need this information. This eliminates the need for users to manually estimate reading time based on page counts, providing accurate predictions immediately during book browsing
Solution Approach 2:
The system replaces the mechanical calculation of reading time (pages divided by reading speed) with a data-driven predictive model based on actual historical reading time measurements. This substitution achieves more accurate estimates without requiring users to perform manual calculations or make assumptions about their reading speed
4Measurement precision
If users manually review each book to find suitable options, then selection accuracy may improve, but excessive time and effort are consumed
Solution Approach 1:
The system uses historical reading completion data as feedback to predict which books users are likely to complete. This feedback loop allows the system to automatically filter and recommend books with high complete-reading probability, achieving accurate selection without requiring extensive manual review by users
Solution Approach 2:
The system performs automatic book selection and filtering based on user preferences and historical data, eliminating the need for users to manually evaluate each book. The self-service mechanism analyzes complete-reading probability and expected time to present optimized book recommendations that match user preferences
Data Source
AI summary
User interface for retrieving information on an e-book is provided. The server acquires information on complete-reading probabilities including information on a first complete-reading probability to information on an n-th complete-reading probability and information on complete-reading expected times including information on a first complete-reading expected time to information on an n-th complete-reading expected time; provides a first UI element capable of allowing the information on the complete-reading probabilities to be set and a second UI element capable of allowing the information on the complete-reading expected times to be set; and in response to detecting that information on a specific complete-reading probability range set through the first UI element and information on a specific complete-reading expected time range set through the second UI element are inputted by the user terminal, retrieves information on one or more specific e-books and then provides the retrieved information on the specific e-books to the user terminal.