Importance Level Computation for Web Page Information Display
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Solution Overview
Problem
Conventional online transaction systems fail to consider the importance of user information for purchasers, leading to inefficiencies in product selection and reduced matching rates.
Innovation Solution
A computation device and method that computes an importance level for displaying information on a web page, taking into account both human information (user information) and thing information (product information), based on user behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional product recommendation based on purchase history is used, then product selection can be automated, but it does not consider which information the purchaser places importance on, leading to increased selection time and decreased convenience
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and information preferences before product recommendation. By pre-computing importance levels for different information types (product information vs. seller information) based on historical behavior, the system prepares personalized weighting schemes in advance, enabling both automated recommendation and user-specific prioritization without increasing selection time
Solution Approach 2:
The system applies different importance weights to different types of information based on individual user preferences. Some users place higher importance on product information (price, specifications) while others prioritize seller information (reputation, transaction history). The recommendation algorithm dynamically adjusts the quality and weighting of different information components for each user, delivering personalized recommendation quality rather than uniform treatment
2Device complexity
If user information is not considered in updating degree of interest, then calculation is simpler, but it reduces matching rate as user information is important for secure transactions
Solution Approach 1:
The system segments information into two distinct categories: product information (price, specifications, images) and seller information (reputation, transaction history, user ratings). By separately tracking and analyzing user interactions with each segment, the system maintains distinct importance levels for different information types. This segmentation allows for manageable calculation complexity while comprehensively capturing user preferences for both product and seller attributes, thereby improving matching accuracy
3Loss of information
If all information is displayed equally on web page, then information completeness is maintained, but important information is not highlighted leading to increased user effort in finding relevant information
Solution Approach 1:
The web page display system applies local quality enhancement by dynamically highlighting and prioritizing display of information types that are most important to each individual user. Based on the computed importance levels, the system enhances the visual prominence, positioning, or formatting of preferred information types (e.g., displaying seller reputation more prominently for trust-oriented users, or product specifications for detail-oriented users). This selective enhancement maintains complete information availability while reducing user effort and time to locate and process important information
Data Source
AI summary
The present invention provides a computation device or the like that computes an index (hereinafter also referred to as “importance level”) used to control display of information of a web page browsed when a purchaser considers product purchase in an online transaction. The computation device includes an importance level computation unit that computes an importance level that is an index indicating how much importance a user who intends to purchase a product places on human information and thing information by using an operation log of a web page and human thing information, in which the human information is information regarding a user who sells a product, and the thing information is information regarding a product, and the human thing information is information indicating whether each piece of content on the web page is the human information or the thing information.