Achievement Recommendation Engine Using Ordered Data Structures
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Solution Overview
Problem
Current methods fail to effectively utilize achievement information from electronic games and services to provide personalized recommendations to users, lacking efficient systems to order and prioritize achievements based on user data.
Innovation Solution
The development of computer-implemented methods that analyze user achievement data to generate ordered lists, assign order values, construct data structures representing achievement dependencies, and recommend titles and achievements based on user preferences and interests, using techniques such as graph-based methods and collaborative filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If achievement information is collected and analyzed to provide personalized recommendations, then recommendation accuracy and user engagement improve, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the achievement data processing into distinct components: collecting achievement information from multiple sources, organizing achievements into structured data formats, analyzing user achievement patterns separately, and generating recommendations based on analyzed patterns. This segmentation reduces system complexity by breaking down the complex task into manageable modules that can be developed and maintained independently.
Solution Approach 2:
The patent implements preliminary action by pre-processing and organizing achievement data into structured formats before analysis. Achievements are collected, standardized, and stored in organized data structures in advance, so that when user recommendations are needed, the system can quickly query and analyze pre-processed data rather than raw unstructured information, thereby improving recommendation accuracy without proportionally increasing real-time processing complexity.
2Adaptability or versatility
If achievement data from multiple sources is integrated and analyzed, then the comprehensiveness of user profiles improves, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by collecting and organizing achievement data from multiple sources in advance into standardized structured formats. User achievement histories are pre-processed and stored in organized data structures, enabling fast querying and analysis when generating recommendations, thus achieving comprehensive user profiles without excessive real-time processing delays.
Solution Approach 2:
The patent uses copying by creating structured data representations of achievement information from various sources. Instead of processing raw data from multiple sources each time a recommendation is needed, the system creates organized copies of achievement data in standardized formats that can be efficiently queried and analyzed, reducing processing time while maintaining data comprehensiveness.
3Productivity
If structured data formats are used to organize achievement information, then data analysis efficiency improves, but data structure complexity increases
Solution Approach 1:
The patent segments achievement data into distinct structured components such as user profiles, achievement records, metadata fields, and relationship structures. Each segment serves a specific analytical purpose and can be independently processed, improving data analysis efficiency while keeping individual structure elements manageable rather than creating one monolithic complex structure.
Solution Approach 2:
The patent implements universal data structures that can handle multiple types of achievement information from different sources in a unified format. The structured data format is designed to be multi-functional, accommodating various achievement types, sources, and user contexts without requiring separate complex structures for each case, thereby improving analysis efficiency without proportionally increasing structure complexity.
Data Source
AI summary
A method performed by one or more computing devices. The method uses data identifying a plurality of users and a plurality of achievements. The data identifies which of the plurality of achievements each of the plurality of users has earned and when any such achievements were earned. The method includes ordering the plurality of achievements to define an ordered achievement list, and for each of the plurality of users, removing from the ordered achievement list any of the plurality of achievements identified in the data as having been earned by the user to define a recommended achievement list for the user. The method also includes identifying a selected achievement, and identifying as a set of similar users any of the plurality of users whose recommended achievement list includes the selected achievement.