Influencer Recommendation Matching for Higher Item Conversion

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Solution Overview

Problem

Existing methods of selecting influencers for item recommendations based solely on follower count lead to a low recommendation success rate, as a large number of followers does not necessarily indicate strong recommendation ability.

Innovation Solution

Determine the recommendation ability of influencers by computing a recommendation matching degree and identifying a superior item category, then select the target influencer based on this ability to improve the recommendation success rate.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If influencers are selected based solely on follower count, then the selection process is simple and fast, but the recommendation success rate is low

Engineering Contradiction:

Improveselection speedVSAvoidrecommendation success rate

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent transforms the single parameter selection (follower count) into multi-parameter evaluation by introducing recommendation matching degree calculation and superior item category identification, thereby improving recommendation accuracy while maintaining automated processing efficiency

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces manual influencer evaluation with an automated computing system that calculates recommendation matching degrees and identifies superior categories through algorithmic processing, achieving both speed and accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If multi-dimensional influencer evaluation is implemented, then recommendation accuracy is improved, but system complexity increases

Engineering Contradiction:

Improverecommendation success rateVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent divides the complex evaluation process into distinct modules: candidate object determination, superior category identification, and recommendation matching degree calculation. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining comprehensive evaluation capabilities

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12488377B2Method, apparatus, device and medium for processing item recommendation

Publication Date: 2025.12.02 BEIJING YOUZHUJU NETWORK TECH CO LTD

AI summary

The embodiments of the disclosure relates to a method, apparatus, device, and medium for item recommendation processing, wherein the method includes: in response to a recommendation request for an item recommendation initiation object, determining at least one candidate recommendation initiation object and a superior recommendation item category of each candidate recommendation initiation object; computing a recommendation matching degree between an item to be recommended and each candidate recommendation initiation object, respectively; determining a target recommendation initiation object for the item to be recommended in the at least one candidate recommendation initiation object according to the superior recommendation item category and the recommendation matching degree of each candidate recommendation initiation object; recommending the item to be recommended to the target recommendation initiation object, so that the target recommendation initiation object performs recommendation processing on the item to be recommended.