Emoji Recommendation System Using Social Behavior Correlation
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Solution Overview
Problem
The selection efficiency of emojis is low because they are sorted by downloads or release time, requiring users to browse a large quantity to find favorites.
Innovation Solution
An emoji recommendation method and apparatus that uses social behavior information, including social relationship chains and emoji usage data, to calculate a correlation degree between users and emojis, recommending emojis based on this data for improved relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If emojis are sorted by downloads or release time, then the emoji shop portal can be simply organized, but the user selection efficiency becomes low
Solution Approach 1:
The patent changes the sorting parameter from objective metrics (download count, release time) to a user-specific parameter (correlation degree calculated from social behavior information). This allows the emoji list to be dynamically reorganized based on each user's preferences and social patterns, improving selection efficiency without complicating the underlying organization system
Solution Approach 2:
The system incorporates feedback loops by analyzing user social behavior information (emoji usage patterns, social relationships) and using this feedback to calculate correlation degrees. This feedback mechanism enables the system to learn and adapt to user preferences over time, continuously improving recommendation accuracy and selection efficiency
2Device complexity
If emojis are sorted by downloads or release time, then the sorting algorithm remains simple, but users must browse a large quantity of emojis to find favorites
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing social behavior information, including emoji usage patterns and social relationship data. This pre-processing allows the system to quickly retrieve and sort emojis based on correlation degrees when needed, avoiding the need for users to manually browse through large quantities of emojis
Solution Approach 2:
The patent transforms the sorting criterion from simple chronological or popularity-based parameters to a sophisticated correlation degree parameter that incorporates multiple dimensions of user behavior. This parameter change enables more precise targeting of user preferences, reducing the number of emojis users need to browse
Data Source
AI summary
This application discloses an emoji recommendation method and apparatus. The method includes: displaying a first user interface of a social network client application; detecting occurrence of a trigger event that corresponds to a preset condition for triggering delivery of an emoji recommendation from the social network server to a first user; retrieving one or more emoji recommendations from the social network server, wherein the one or more emoji recommendations are selected from an emoji library for the first user in accordance with social behavior information of the first user; and displaying the one or more emoji recommendations on the first user interface, including displaying one or more editing controls to adjust one or more parameters for calculating a correlation degree of the user and a respective emoji based on one or more attributes of the one or more emoji recommendations retrieved from the social network server.