Multimodal Content Retrieval for Ambiguous Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content retrieval systems struggle with ambiguous queries, as exact match similarity measures and statistical methods fail to produce quality results when users provide vague ideas of desired content.
Innovation Solution
A content retrieval system that employs multimodal analysis to identify relevant content items by generating similarity scores based on embeddings of different data types, normalizing these scores, and mapping tokenized keywords from queries to content descriptions to determine the most relevant items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact match similarity measures are used for content retrieval, then retrieval precision is improved for specific queries, but retrieval effectiveness deteriorates for ambiguous queries
Solution Approach 1:
The patent segments the retrieval process into multiple independent similarity measure components: exact match similarity measure, fuzzy match similarity measure, and statistical similarity measure. Each component operates independently to evaluate different aspects of query-content匹配, allowing the system to leverage the strengths of each measure while mitigating their individual weaknesses through combination.
Solution Approach 2:
The patent merges multiple similarity measures (exact match, fuzzy match, statistical) into a unified retrieval framework. By combining these different measurement approaches, the system achieves both high precision for specific queries and robust effectiveness for ambiguous queries, as each measure compensates for the limitations of the others.
2Device complexity
If traditional statistical methods are used for content retrieval, then system complexity is reduced, but retrieval quality deteriorates for ambiguous queries
Solution Approach 1:
The patent segments the complex retrieval task into multiple specialized similarity measure modules (exact match, fuzzy match, statistical). This segmentation allows each module to focus on specific aspects of query interpretation, improving overall retrieval quality for ambiguous queries while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent creates a universal retrieval framework that incorporates multiple similarity measures capable of handling diverse query types. This multi-functional system can effectively process both specific and ambiguous queries using the same integrated architecture, eliminating the need for separate specialized systems.
Data Source
AI summary
A content retrieval system may receive a query associated with a plurality of content items in a repository. For each content item of the plurality of content items: a respective first and second similarity score may be generated based on a similarity between embeddings indicative of a first and second data type generated from the query and for the content item; and a respective normalized similarity score may be generated based on a combination of the respective first and second similarity scores. A set of content items with respective normalized similarity scores that satisfy a similarity score threshold may be identified. An exact-match (lexical) search may yield respective mapping scores for content items that may also be ranked. An output indicative of content items that are identified in the set of content items with high-ranking similarity scores and identified in the set of content items with high-ranking mapping scores.