Theme Recommendation System for User Engagement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing application recommendation methods fail to effectively arouse user interest and generate profits by simply estimating and recommending applications without providing multiple options or themes, leading to limited user engagement.
Innovation Solution
A theme recommendation method and apparatus that collects historical user operations, predicts user preferences, classifies applications into themes, and pushes themes with high preference values to users, allowing multiple options and increased user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If applications are simply estimated and recommended to users without theme classification, then the recommendation process is simple and fast, but user interest is not aroused and profitability is limited
Solution Approach 1:
The patent segments the recommendation system into two levels: theme level and application level. Applications are first grouped into themes based on classification data, then recommendations are made at the theme level. This segmentation allows the system to provide structured, thematic recommendations that engage users more effectively while maintaining operational efficiency through automated classification.
2Adaptability or versatility
If multiple applications within themes are recommended to users, then user choice and interest are enhanced, but the complexity of the recommendation system increases
Solution Approach 1:
The system performs preliminary classification of applications into themes in advance, storing this classification data for future use. When making recommendations, the system retrieves pre-established themes and their associated applications, rather than performing complex classification computations in real-time. This preliminary action reduces online computational complexity while enabling versatile thematic recommendations.
Solution Approach 2:
The patent introduces themes as an intermediary layer between the recommendation engine and users. Instead of directly recommending individual applications, the system recommends themes that contain multiple applications. This intermediary structure simplifies the recommendation process while providing users with organized, interest-based groups of applications, enhancing both user choice and system manageability.
3Measurement precision
If theme classification is performed on to-be-recommended items, then recommendations become more targeted and effective, but the processing time and computational resources increase
Solution Approach 1:
The system performs theme classification in advance and stores the classification results. When generating recommendations, it retrieves pre-computed theme assignments rather than performing classification computations in real-time. This preliminary classification action enables accurate, targeted recommendations while minimizing processing time during actual recommendation delivery.
Data Source
AI summary
The method includes: collecting historical operations of sample users for M items, and predicting a preference value of a target user for each of the M items according to historical operations of the sample users for each of the M items, collecting classification data of N to-be-recommended items, and classifying the N to-be-recommended items according to the classification data of the N to-be-recommended items, to obtain X themes, where each of the X themes includes at least one of the N to-be-recommended items, and the N to-be-recommended items are some or all of the M items; calculating a preference value of the target user for each of the X themes according to a preference value of the target user for a to-be-recommended item included in each of the X themes; and pushing a target theme to the target user.