Live Room Product Recommendation System Using Pre-Computed Candidate Libraries
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Solution Overview
Problem
In network livestreaming, there is a mismatch between products sold in live rooms and audience requirements, leading to low-quality products and reduced transaction conversion rates.
Innovation Solution
A product recommendation method that generates a candidate product library based on interaction data, selects customizable products, configures customizable information, and adds them to the live room, allowing users to customize products for better matching and increased conversion rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional product selling methods are used in live rooms, then the operation process is simple, but the matching between products and audience requirements is poor leading to low transaction conversion rates
Solution Approach 1:
The system performs preliminary actions by generating a candidate product library before the live stream, selecting customizable products in advance, and pre-configuring product information. This allows the live room to offer personalized products without complex real-time processing during the broadcast, thereby improving conversion rates while keeping the operational complexity manageable.
Solution Approach 2:
The system enables self-service through automated product recommendation based on audience portraits and interaction data. The platform automatically selects and configures products without requiring manual intervention during the live stream, improving transaction conversion rates while the complexity is managed through algorithmic automation rather than human complexity.
2Adaptability or versatility
If customizable products are selected and configured, then the matching between products and audience needs is improved, but the operation process becomes more complex
Solution Approach 1:
The system segments the product configuration process into distinct stages: generating the candidate product library, selecting customizable products, configuring customizable information, and adding to the live room. This segmentation allows each stage to be optimized independently, improving adaptability while making the overall operation more manageable through structured processes.
Solution Approach 2:
The system introduces an intermediary product recommendation system that mediates between the available products and audience requirements. This intermediary layer automatically processes interaction data, generates recommendations, and configures products, thereby improving adaptability while shielding users from the complexity of direct configuration operations.
3Reliability
If products are selected based on interaction data, then the quality of recommended products is improved, but the data processing complexity increases
Solution Approach 1:
The system performs data processing and product selection in advance before the live stream begins. By pre-processing interaction data and generating the candidate product library beforehand, the system improves recommendation quality without requiring complex real-time data processing during the broadcast, thus managing the complexity effectively.
Solution Approach 2:
The system creates a simplified representation of the complex data processing logic through pre-computed product recommendations and candidate libraries. This copying approach allows high-quality recommendations to be delivered without exposing the complexity of the underlying data processing systems, maintaining reliability while managing complexity.
Data Source
AI summary
The present disclosure techniques for implementing livestreaming. The techniques comprise generating a candidate product library associated with a live room, wherein the candidate product library comprises a plurality of candidate products, and wherein the live room is configured to implement livestreaming by a streamer terminal device associated with an online streamer; generating recommendation indicators corresponding to the plurality of candidate products based on historical interaction data of the plurality of candidate products; causing to present the recommendation indicators on the streamer terminal device; selecting at least one customizable product from the plurality of candidate products based at least in part on the recommendation indicators; configuring customizable information of the at least one customizable product; and adding the at least one customizable product configured with the customizable information to the live room for pushing to one or more audience terminal devices during a livestreaming process.