Information Recommendation Ranking for Faster User Level Upgrades
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Solution Overview
Problem
Existing information recommendation methods fail to address the needs of users seeking to quickly upgrade their levels and enjoy more rights by ignoring the difficulty of achieving level upgrades based on user interactions.
Innovation Solution
Determine a historical interaction parameter and a target interaction parameter for each candidate object, rank the objects based on these parameters, and recommend information accordingly to assist users in easily upgrading their levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing information recommendation methods are used, then the system can provide basic recommendation services, but users cannot quickly upgrade their levels and access more rights
Solution Approach 1:
The patent changes the recommendation parameters from traditional user preference-based metrics to include level upgrade difficulty parameters. By introducing new parameters (level upgrade difficulty, user current level, target level requirements) and modifying the recommendation algorithm to weigh these parameters, the system enables users to quickly identify and access information that helps them upgrade levels, thereby resolving the contradiction between recommendation effectiveness and system complexity.
2Adaptability or versatility
If the recommendation system considers more user needs and factors, then user satisfaction improves, but the system complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: a parameter acquisition module that collects user level and interaction data, a difficulty calculation module that computes level upgrade difficulty, and a recommendation generation module that produces personalized recommendations. This segmentation allows the system to handle multiple user needs (level upgrading, information relevance) through specialized sub-components, improving adaptability while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary calculations of level upgrade difficulty and target level requirements before generating recommendations. By pre-computing these parameters and storing them for quick retrieval, the system can rapidly adapt recommendations to user needs without performing complex real-time calculations, thereby enhancing versatility while controlling system complexity.
Data Source
AI summary
The disclosure relates to a method for information recommendation, an apparatus, an electronic device, and a storage medium. The method includes: determining a historical interaction parameter of a target user for associated information of each candidate object in a candidate object set; determining a target interaction parameter corresponding to each candidate object; ranking the candidate objects in the candidate object set based on the historical interaction parameter of the target user for the associated information of each candidate object and the target interaction parameter corresponding to each candidate object, to obtain a ranking result; and recommending, based on the ranking result, the associated information of the candidate objects to the target user.