Media Content Matching via Segmented Fuzzy Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing diversity of media content providers and delivery modes complicates the task of consolidating and accurately matching media content with shared attributes, such as title, in an aggregation database, leading to difficulties in navigation and content selection for users.
Innovation Solution
A system and method for managing an aggregation database of media guidance information, where a server receives a primary media content record and compares its attributes with candidate records, computes attribute scores, and links the primary record to the most relevant candidate record based on composite scores, using fuzzy matching and binary comparisons, and updates or merges records as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional exact matching methods are used to match media content records, then matching speed is fast, but matching accuracy deteriorates when dealing with diverse delivery modes and related programs sharing common attributes
Solution Approach 1:
The matching process is divided into multiple stages: first filtering candidate records using title attribute comparison, then computing composite scores based on multiple attributes (release year, genre, director, etc.), and finally performing binary comparison on discrete attributes. This segmented approach improves matching accuracy by considering multiple factors while maintaining manageable system complexity through phased processing.
Solution Approach 2:
The system computes composite scores by assigning different weights to various attributes (title, release year, genre, director, cast, etc.), allowing flexible parameter adjustment. This enables the system to adapt to different matching scenarios and improve accuracy by emphasizing more important attributes while maintaining reasonable system complexity through configurable parameters.
2Measurement precision
If multiple attributes are compared to improve matching accuracy, then matching precision improves, but processing time increases
Solution Approach 1:
The comparison process is segmented into hierarchical stages: title attribute filtering first, then composite score computation for remaining candidates, and finally binary comparison on discrete attributes. This segmentation reduces processing time by eliminating non-matching records early while maintaining precision through progressive filtering.
Solution Approach 2:
The system performs full multi-attribute comparison only on a subset of candidate records that pass the initial title filtering stage. For most records, only partial comparison (title attribute) is performed, significantly reducing overall processing time while maintaining matching precision for the final selection.
3Reliability
If comprehensive attribute comparison is performed for all candidate records, then matching reliability improves, but computational complexity increases
Solution Approach 1:
The candidate record set is segmented into different processing stages: initial filtering by title attribute, intermediate scoring based on composite attributes, and final verification by binary comparison. This segmentation maintains matching reliability through comprehensive comparison of relevant records while reducing computational complexity by excluding non-matching records from detailed analysis.
Solution Approach 2:
Different levels of comparison depth are applied to different candidate records based on their relevance. Records with matching titles receive full multi-attribute comparison, while non-matching records receive only title filtering. This local quality approach ensures high reliability for true matches while reducing overall computational complexity.
Data Source
AI summary
Methods and systems are described herein for managing an aggregation database. Matching rules that describe filters may be defined to determine how to match a media content record received from an external source to a stored record in the aggregation database. Fuzzy matching may be used to match attribute fields of the received record and stored records. Based on the results of the fuzzy matching, the received primary media content record may be linked to a stored record in the aggregation database.