Enterprise Recommendation System for New Employee Onboarding
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Solution Overview
Problem
New employees face difficulties in efficiently using enterprise service platforms, leading to reduced work efficiency due to the complexity of diversified service projects, requiring them to consult colleagues frequently.
Innovation Solution
A recommendation system that performs data mining using a user profile building module, a task profile building module, and a profile matching module to generate structured data, providing intelligent recommendations by matching user and task profiles with input information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprise service platform provides diversified service projects, then service coverage is improved, but system complexity increases making it difficult for users to operate
Solution Approach 1:
The system automatically generates user profiles and task profiles through data mining, and performs matching without human intervention. The recommendation engine self-adjusts by continuously learning from user behavior data, reducing the need for manual system configuration and making the complex system easier to operate.
Solution Approach 2:
The patent introduces a recommendation system as an intermediary between users and the enterprise service platform. This intermediary processes user input information, matches it with relevant tasks from the diversified service projects, and presents simplified recommendations to users, thereby reducing the perceived system complexity.
2Adaptability or versatility
If enterprise service platform includes diversified service projects, then service functionality is improved, but user operation efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-building user profiles and task profiles through data mining of historical behavior information before users need them. The recommendation engine prepares matched task recommendations in advance, so when users need services, the system quickly presents relevant options without requiring users to search through diversified service projects manually.
Solution Approach 2:
The system implements feedback mechanisms by continuously mining user behavior information and updating user profiles and task profiles accordingly. This feedback loop enables the system to learn from user interactions with diversified service projects and improve recommendation accuracy over time, thereby improving user operation efficiency.
3Reliability
If new employees consult colleagues on platform operation, then operational accuracy is improved, but work efficiency decreases
Solution Approach 1:
The recommendation system enables new employees to independently obtain accurate task guidance through automated profile matching, without needing to consult colleagues. The system self-provides operational accuracy by matching user profiles with relevant task profiles based on historical behavior information, allowing employees to work efficiently from the start.
Solution Approach 2:
The system performs preliminary data mining to build comprehensive user profiles and task profiles before new employees need operational guidance. This preliminary action ensures that accurate task recommendations are ready in advance, eliminating the need for time-consuming consultations while maintaining high operational accuracy.
Data Source
AI summary
A recommendation system and an operation method thereof are provided. The recommendation system includes a storage device and a processor. The storage device stores an enterprise database of an enterprise system. The processor executes a plurality of modules in the storage device. A user profile building module and a task profile building module perform data mining according to historical behavior information, user information and task information in the enterprise database to generate user profile data and task profile data. The profile matching module performs data mining according to the historical behavior information, the user profile data, and the task profile data to generate a profile knowledge map, so that when the enterprise system 10 receives input information, the enterprise system obtains recommended data that matches the input information from the user profile data, the task profile data, and the profile knowledge map, so as to improve work efficiency.