Information Recommendation Ranking Using Upgrade Difficulty Parameters
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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.
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 enjoy more rights
Solution Approach 1:
The system pre-calculates and stores the interaction parameters required for each user level upgrade before the user actually attempts to upgrade. This includes determining the historical interaction parameters the user has already accumulated and the target interaction parameters needed for the next level, allowing the system to proactively identify and recommend objects that will help the user achieve the upgrade most efficiently.
Solution Approach 2:
The system introduces new parameters (historical interaction parameter and target interaction parameter) to quantify and evaluate the difficulty of level upgrades. By changing from a generic recommendation approach to one based on these specific parameters, the system can objectively measure upgrade difficulty and recommend objects that optimize the user's progression path.
2Productivity
If the system recommends objects without considering level upgrade difficulty, then the recommendation process is simple, but users miss opportunities for efficient level advancement
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: a parameter determination module that calculates historical and target interaction parameters, a difficulty evaluation module that assesses upgrade difficulty based on these parameters, and a recommendation module that selects objects. This segmentation allows the complex task of efficient level advancement to be broken down into manageable, independent components.
Solution Approach 2:
The system implements feedback by continuously monitoring the user's interaction parameters and comparing them against the target parameters required for level upgrades. Based on this feedback, the system dynamically adjusts its recommendations to guide the user toward objects that will most effectively close the gap between current and target parameters, creating a closed-loop optimization system.
Data Source
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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.