E-book Gifting Relationship Analysis for Customized Recommendations
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Solution Overview
Problem
E-book services fail to provide customized information effectively by not utilizing human relationships formed through e-book gifting and exchange among users, despite increasing trends in e-book consumption and social networking.
Innovation Solution
A method and server system that collects information on e-book gifting and reading habits to assess user relationships, providing personalized recommendations by setting relationships based on gift direction, frequency, and price, and offering customized information services based on the degree of involvement in reading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If e-book service provides customized information based only on individual buying/borrowing history, then service simplicity is maintained, but information customization effectiveness deteriorates
Solution Approach 1:
The patent merges individual reading history with social relationship data (gifting patterns, exchange relationships) to create a comprehensive user profile. This combination enables more effective customization by integrating multiple data sources that reveal both personal preferences and social context, thereby improving information relevance without overwhelming complexity
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes social relationship data and reading behaviors to generate customized information recommendations. This intermediary layer acts as a mediator between raw data and final recommendations, filtering and synthesizing information to improve customization effectiveness while managing system complexity
2Adaptability or versatility
If e-book service ignores user relationships formed through gifting, then data processing simplicity is maintained, but service adaptability deteriorates
Solution Approach 1:
The patent segments user data into distinct components: individual reading history, gifting behavior patterns, and relationship metrics. By dividing the data structure into manageable segments, the system can process each type of information separately and then integrate them, thereby improving adaptability to different user contexts while controlling processing complexity
Solution Approach 2:
The patent changes the parameters used to describe user interactions by introducing relationship-based metrics (e.g., gifting frequency, exchange relationships) alongside traditional reading metrics. This parameter expansion enables the service to adapt to diverse user scenarios and relationship types, enhancing versatility without proportionally increasing complexity
3Measurement precision
If e-book service collects comprehensive user relationship data, then recommendation accuracy is improved, but information processing time increases
Solution Approach 1:
The patent performs preliminary processing of user relationship data by pre-calculating relationship metrics (such as friendship scores, gifting patterns) and storing them in an optimized format. This preliminary action prepares the data in advance, enabling faster retrieval and processing during recommendation generation, thereby improving accuracy without proportionally increasing processing time during actual use
Solution Approach 2:
The patent extracts only the most relevant relationship features and reading behavior metrics needed for accurate recommendations, rather than processing all available data. By selecting and extracting key parameters (such as gifting frequency, reading completion rates, relationship strength), the system achieves high recommendation accuracy while minimizing processing time through focused data analysis
Data Source
AI summary
A method includes the steps of: (a) setting a relationship between users by referring to at least one piece of information on direction between the user who gives an e-book as a gift and the other user who receives it, information on the number of times the gift has been provided, and information on a price of the gift; (b) judging a first user and a second user whose degree of friendliness exceeds a preset value; and (c) acquiring information on a degree of involvement in reading that shows to which level the second user has read the gift, if the gift is provided from the first user to the second user; and (d) providing a service by referring to information on the gift, if the information on the degree of involvement in reading is judged to exceed a prefixed value.