Semantic Ad Keyword Purchasing for Higher ROI Targeting
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Solution Overview
Problem
Conventional search engines struggle to accurately identify and provide relevant information to users due to the vast volume of digital content and the mismatch between user search queries and content terminology, often resulting in irrelevant search results.
Innovation Solution
Utilizing semantic networks to represent user context information and enhance search results by merging reference and target knowledge representations, allowing for semantic relevance-based advertisement purchasing and improved content selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use keyword matching to retrieve information, then the system complexity remains low, but the relevance accuracy of search results deteriorates due to mismatch between user queries and content terminology
Solution Approach 1:
The patent introduces semantic networks as an intermediary layer between user queries and content. The semantic network represents concepts and their relationships, allowing the system to translate user queries into semantic concepts and match them with content based on semantic similarity rather than direct keyword matching, thereby improving relevance accuracy without requiring complete system redesign
Solution Approach 2:
The patent changes the matching parameter from literal keyword equality to semantic similarity. By representing both queries and content as semantic concepts with associated attributes, the system can calculate semantic similarity scores and retrieve results based on this transformed parameter, resolving the contradiction between simplicity and accuracy
2Productivity
If advertisers target users with advertisements based on traditional demographics, then the advertising system remains simple, but the return on investment deteriorates due to inability to accurately target users with relevant content
Solution Approach 1:
The semantic network serves multiple functions: it represents user interests, indexes content semantically, and enables both search query processing and advertisement targeting. This multi-functionality allows the system to improve advertising relevance using the same semantic infrastructure already in place for search, thereby increasing ROI without proportionally increasing system complexity
Solution Approach 2:
The system uses user interaction feedback (search queries, viewing behavior) to continuously refine and update the semantic representation of user interests. This feedback mechanism allows the advertising system to learn user preferences over time and improve targeting accuracy, increasing return on investment through adaptive learning rather than complex predetermined targeting rules
3Measurement precision
If the system processes and analyzes large volumes of digital content to improve search relevance, then the quality of information retrieval improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary semantic analysis and indexing of content in advance, building semantic networks and concept representations before actual search queries are received. This pre-processing allows the system to quickly retrieve and match content based on pre-computed semantic relationships during actual search operations, improving retrieval quality while reducing real-time processing time
Data Source
AI summary
Disclosed is a system and method for enhancing value for advertisers by helping them select and initiate the purchase of an advertisement associated with an advertising (ad) words or phrases that have strong semantic relationships to a given context, but which are not necessarily the most popular ad words or phrases with the highest costs. Advertisements associated with ad words or phrases that have strong semantic relationships to a given context, and yet are still cost effective in that their calculated value exceeds the costs of purchasing the ad keywords, are bid for and bought. In an embodiment, the system and method may be adapted to automatically purchase advertisements associated with ad words or phrases when the ad words or phrases fall within a desired price range based on their calculated value. As the prices of advertisements associated with these words or phrases fluctuate over time based on their popularity of the words or phrases, the automated bidding and buying of advertisements may be used to purchase advertisements associated with words or phrases at a price desirable to a given ad purchaser. By automatically purchasing such advertisements, the return on investment (ROI) for an advertiser may be improved.