User Affinity Determination via Service Order Segmentation
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Solution Overview
Problem
Existing on-demand service systems lack an efficient method to determine user affinity, which is crucial for providing personalized recommendations based on historical service orders and location preferences.
Innovation Solution
A system and method that analyze historical service orders within a predetermined time frame, selecting relevant orders based on time and location thresholds to calculate user affinity, and adjust affinities based on shared wireless LAN connections and user relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system analyzes all historical service orders to determine user affinity, then the accuracy of affinity determination is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the historical service orders into multiple dimensions including time periods, location clusters, service types, and user relationships. By dividing the large dataset into manageable segments and analyzing them separately, the system maintains high affinity determination accuracy while reducing overall computational complexity. Each segment can be processed independently and efficiently.
Solution Approach 2:
The system performs preliminary actions by pre-processing and indexing historical service order data before affinity determination. Service orders are pre-categorized by location, time, and other attributes, creating an optimized data structure that enables fast querying and analysis. This preliminary organization significantly reduces processing time during actual affinity calculations.
2Adaptability or versatility
If the system considers multiple dimensions (time, location, relationships) for affinity calculation, then the personalization quality is improved, but the data processing requirements increase
Solution Approach 1:
The patent introduces multiple dimensional perspectives for analyzing service orders, including temporal dimensions (time periods, frequency), spatial dimensions (location clusters, distance), and relational dimensions (user relationships, social connections). By analyzing data across these multiple dimensions simultaneously, the system achieves high personalization quality without proportionally increasing processing volume, as each dimension provides complementary insights.
Solution Approach 2:
The system creates a multi-functional affinity calculation framework that handles various types of service order data uniformly. The same processing pipeline can analyze different dimensions (time, location, relationships) using consistent methods, reducing redundant processing. This universal approach allows the system to consider multiple dimensions efficiently without requiring separate specialized processing for each dimension.
3Measurement precision
If the system processes service orders with strict time and location thresholds, then the relevance of recommendations is improved, but the number of matching orders decreases
Solution Approach 1:
The patent dynamically adjusts processing parameters including time thresholds, location distance thresholds, and weighting factors based on the specific analysis context. By optimizing these parameters, the system maintains high recommendation relevance while ensuring sufficient matching orders are found. The parameters can be tuned to balance between precision and quantity of matches.
Solution Approach 2:
The system initially applies strict time and location thresholds to identify highly relevant service orders, then progressively relaxes these criteria to include additional matching orders. This partial action approach ensures that the most relevant orders are prioritized while still gathering sufficient data for accurate affinity determination. The system processes orders in stages, from most to least relevant.
Data Source
AI summary
The present disclosure relates to systems and methods for determining an affinity between a target user and at least one candidate user. The systems may perform the methods to obtain a plurality of target service orders associated with the target user and a plurality of candidate service orders associated with the candidate user within a predetermined time period; select one or more relevant service orders from the plurality of candidate service orders based on the plurality of target service orders; and determine an affinity between the target user and the candidate user based on the one or more relevant service orders.