Item Selection Apparatus Using Dynamic User Preference Parameters
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Solution Overview
Problem
Existing methods for recommending items to users are cumbersome, requiring complex parameter settings that are difficult for users and service operators to manage, leading to reduced recommendation accuracy over time due to static parameter settings that fail to adapt to changing user preferences.
Innovation Solution
An item selecting apparatus that calculates usage characteristics from user history data to automatically set parameters for selecting items, using freshness values, novelty indices, and popularity indices to recommend items based on user-specific preferences, eliminating the need for manual parameter setting and ensuring accurate recommendations even as user preferences change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameters (learning period, recommendation period, number of similar users) are set for each user to accommodate individual differences in taste change rates, then recommendation accuracy is improved, but operation complexity increases and ease of operation deteriorates
Solution Approach 1:
The system automatically determines user-specific parameters (learning period, recommendation period, number of similar users) by analyzing user behavior data without requiring manual input. The parameter setting process serves itself by using usage history to compute appropriate values, eliminating the need for user or operator intervention in parameter configuration.
Solution Approach 2:
The system dynamically adjusts parameters based on user characteristics and behavior patterns. Instead of fixed parameters for all users, the system computes individualized parameter values that adapt to each user's taste change rate and usage patterns, thereby maintaining high recommendation accuracy across diverse user groups.
2Measurement precision
If parameters are manually set by service operators for each user, then recommendation accuracy can be optimized, but time consumption and productivity are reduced due to the troublesome operation
Solution Approach 1:
The parameter determination process is automated through self-service mechanisms where the system computes appropriate parameters by analyzing user behavior data. This eliminates the need for service operators to manually configure parameters for each user, dramatically improving productivity while maintaining recommendation accuracy.
Solution Approach 2:
The manual mechanical process of parameter setting by operators is replaced with an automated computational system that analyzes usage history and behavior patterns to determine parameters. This substitution of mechanical operation with automated information processing restores productivity while preserving recommendation quality.
3Ease of operation
If parameters are established once and not updated, then ease of operation is improved, but recommendation accuracy deteriorates over time as user preferences change
Solution Approach 1:
The system implements dynamic parameters that are continuously updated based on user behavior data. Instead of static parameters set once, the parameters adapt over time to reflect changing user preferences, maintaining recommendation accuracy without requiring manual reconfiguration. The system automatically detects and responds to changes in user taste patterns.
Solution Approach 2:
The system uses feedback from user behavior data to continuously refine and update parameters. By monitoring usage patterns and taste changes, the system adjusts parameters automatically, ensuring that recommendations remain accurate over time. This feedback mechanism eliminates the need for manual parameter updates while maintaining precision.
4Measurement precision
If complex parameter settings are required for each user, then recommendation accuracy can be optimized, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The system extracts and utilizes user behavior data from existing usage history to automatically determine parameters. By taking out the necessary information from readily available usage data, the system avoids the need for complex parameter configuration interfaces or additional user inputs, simplifying the system while maintaining accuracy.
Solution Approach 2:
The system uses a universal approach where the same parameter determination mechanism applies to all users regardless of their individual characteristics. This multi-functional parameter system handles diverse user types (fast changers, slow changers, various usage patterns) through a single automated process, reducing overall system complexity while maintaining individualized accuracy.
Data Source
AI summary
In an item selecting apparatus performing a selection of an item to be recommended for each user, it is performed to calculate, with respect to each usage registration of an item by a user, an elapsed value as a difference between a time point of creating the item or staring providing of the item and a predetermined time point, acquire a usage characteristics of each user based on the elapsed value and calculate a freshness value representing a degree of freshness about each item. Further, using correspondence rules of different characteristics corresponding to the usage characteristics, it is performed to calculate a novelty index by applying the freshness value of each item on the correspondence rule corresponding to the usage characteristics of each user, calculate a priority of the item for each user, based on the novelty index and performing a selection of the item.