App Recommendation Testing Using Simulated User Behavior
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Solution Overview
Problem
Current methods lack an effective way to test the recommendation effect of applications, particularly in simulating user interactions to assess the accuracy of personalized content recommendations.
Innovation Solution
A computer-implemented method and apparatus that simulates user interactions with applications by triggering the display and selection of recommended items, performing semantic recognition, and determining the matching degree between recommended items and user interests to evaluate recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated testing is implemented to evaluate recommendation systems, then testing efficiency is improved, but testing accuracy deteriorates due to inability to truly simulate user behavior
Solution Approach 1:
The patent creates virtual user accounts that copy and simulate real user behaviors, preferences, and interaction patterns. These virtual users are populated with simulated historical data that mirrors authentic user profiles, enabling automated testing while maintaining behavioral realism. The system generates synthetic user accounts with predetermined preferences and interaction histories that accurately represent diverse user segments.
Solution Approach 2:
The patent introduces virtual user accounts as intermediary entities between automated testing systems and recommendation algorithms. These virtual users act as mediators that bridge the gap between machine automation and human-like behavior simulation, allowing automated evaluation while preserving authentic interaction patterns through carefully designed simulated preferences and historical data.
2Measurement precision
If real user data is used for testing, then measurement precision is improved, but user privacy and security are compromised
Solution Approach 1:
The patent creates disposable virtual user accounts that serve as temporary test subjects. These synthetic user profiles can be generated, used for testing, and discarded without any real-world privacy concerns. The virtual users are designed to be sufficient for testing purposes but not valuable enough to target, eliminating privacy risks while maintaining testing authenticity.
Solution Approach 2:
The patent copies essential user characteristics into virtual profiles without using actual user data. By replicating demographic attributes, preferences, and interaction patterns in synthetic form, the system achieves testing accuracy comparable to real data while completely eliminating privacy exposure risks through the use of fictional user identities.
3Measurement precision
If comprehensive user profiles are created for accurate simulation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the virtual user population into distinct groups based on key characteristics such as demographics, preferences, and behavior patterns. This segmentation allows the system to manage complexity by handling user simulations in manageable segments rather than as a monolithic system, enabling scalable testing across diverse user profiles through modular account creation and management.
Solution Approach 2:
The patent performs preliminary population of virtual user accounts with simulated historical data and predetermined preferences before actual testing begins. This advance preparation includes creating realistic interaction histories, setting user profiles, and configuring preferences in advance, which simplifies the testing process by having all simulation parameters ready beforehand rather than generating them during execution.
Data Source
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AI summary
The present application discloses a test method and apparatus for an application, an electronic device, and a storage medium, relates to the field of test technologies, and may be applied to the fields of artificial intelligence and big data technologies. A specific solution includes: obtaining, from a target APP, a first recommended item for being selected, where the target APP is an APP of interest to a target user; triggering the target APP to open a first sub-page corresponding to the first recommended item for being selected when the first recommended item for being selected matches target information of interest, where the target information of interest is information of interest to the target user; and determining a test result for the target APP based on a matching degree between a first target recommended item and the target information of interest, where the first target recommended item includes a recommended item generated by the target APP after the first sub-page is opened on the target APP. According to the technical solutions of the present disclosure, the use of an app by a user may be simulated, so that a recommendation effect of the app is automatically tested.