Commodity-Host Adaptability Model for Live Shopping Selection
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Solution Overview
Problem
In online live shopping, existing data processing technologies face challenges in efficiently selecting commodities due to the variety of products and user groups, as they do not adequately consider host guidance and personalized user requirements, leading to subjective host selections and significant time and resource consumption.
Innovation Solution
A method and apparatus that analyze historical commodity and live-broadcast information to determine host, commodity, and user features, using a commodity-host adaptability classification model based on deep learning algorithms, to generate a personalized list of commodities by dividing information into levels and selecting in-warehouse commodities based on adaptability, emotion curve layering, and user behavior analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hosts subjectively select commodities based on personal preference, then commodity variety can be increased, but selection time and resource consumption increase significantly
Solution Approach 1:
The patent segments the commodity selection process into multiple dimensions including host features (broadcasting style, audience interaction), commodity features (category, price range, sales data), and user features (preferences, purchasing power, browsing behavior). This segmentation enables systematic analysis rather than subjective selection, resolving the contradiction between commodity variety and selection time.
Solution Approach 2:
The system performs preliminary analysis of host features, commodity features, and user features before the actual commodity selection. Historical data is pre-processed to establish feature profiles, and the adaptability classification model is pre-trained. This preliminary action reduces real-time selection time while maintaining high commodity variety.
2Productivity
If traditional data processing technologies are used for commodity selection, then processing speed may be maintained, but personalization and host guidance are not considered
Solution Approach 1:
The patent applies local quality by creating personalized commodity lists tailored to each host's specific features and each user's specific preferences. The system analyzes local characteristics such as host broadcasting style, audience demographics, and individual user behavior patterns to generate customized recommendations, thereby achieving high personalization without sacrificing processing efficiency through the optimized adaptability classification model.
Solution Approach 2:
The system dynamically adjusts selection parameters based on analyzed features. The adaptability classification model uses multiple parameters including host-feature weights, commodity-feature weights, and user-feature weights that are continuously optimized. This parameter-based approach enables both fast processing and high personalization by changing selection criteria according to specific contexts.
3Measurement precision
If comprehensive analysis of host, commodity, and user features is performed, then selection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal adaptability classification model that handles multiple analysis tasks through a single integrated system. The model simultaneously evaluates host features, commodity features, and user features using a unified framework, reducing system complexity compared to multiple separate analysis systems while maintaining high selection accuracy through comprehensive feature integration.
4Adaptability or versatility
If emotion curve layering and multi-level information division are used, then commodity list personalization is enhanced, but computational resources increase
Solution Approach 1:
The system applies partial action by focusing computational resources on the most influential features and time periods. The emotion curve layering method identifies key emotional peaks and valleys in user feedback during broadcasting, and the multi-level information division prioritizes analysis of high-impact commodity categories. This selective approach enhances personalization while controlling computational resource consumption.
Data Source
AI summary
A method and device for information analysis are provided. The method comprises: in response to a received commodity analysis request, obtaining historical commodity information corresponding to the commodity analysis request and live broadcast information; dividing the historical commodity information according to a broadcast starting time point and a broadcast ending time point of a historical commodity to generate commodity information at different levels; analyzing the commodity information at different levels and live broadcast information corresponding to the commodity information at a corresponding level to determine various features of different levels; and according to the various features of different levels, selecting commodities in a warehouse by using a commodity and anchorman adaptive classification model to generate a list of different categories of commodities at different levels.