Search Campaign Restructuring for Theme-Based Keyword Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search campaign datasets are inefficient, leading to keyword redundancies, excessive memory usage, and slow processing times due to duplications and improper mapping of keywords to landing pages.
Innovation Solution
A method involving fuzzy matching, node clustering, and vectorization to reduce and restructure search campaigns by eliminating redundancies, reducing the number of landing pages, and improving keyword mapping efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If initial datasets with multiple groups and keywords are used to build search campaigns, then comprehensive keyword coverage is achieved, but data structure size becomes excessive and processing speed decreases
Solution Approach 1:
The patent merges multiple content groups that share the same theme into a single consolidated group. By identifying thematic overlaps between groups and combining them, the system reduces the total number of groups and eliminates redundant keyword mappings, directly reducing data structure size while improving processing speed through fewer operations required.
Solution Approach 2:
The patent creates a unified theme-based data structure where a single group can serve multiple purposes by mapping to various keywords through thematic relationships. This universal theme-based approach replaces the need for separate dedicated groups for each keyword, reducing overall data structure complexity while maintaining comprehensive keyword coverage.
2Device complexity
If multiple content groups are mapped to the same landing page, then comprehensive content coverage is achieved, but mapping complexity and memory usage increase
Solution Approach 1:
The patent extracts the landing page mapping relationship from the individual content group level and elevates it to the theme level. By determining which themes map to which landing pages at a higher abstraction level, the system eliminates redundant mapping information stored at the group level, reducing memory usage while simplifying the mapping structure.
Solution Approach 2:
The patent introduces a thematic dimension as an intermediate layer between content groups and landing pages. Instead of direct group-to-landing-page mappings, the system uses theme-to-landing-page mappings combined with group-to-theme associations, creating a more efficient hierarchical structure that reduces overall mapping complexity.
3Loss of time
If redundant keywords are included across multiple campaigns, then keyword coverage is maintained, but processing time increases
Solution Approach 1:
The patent merges redundant keywords that share the same theme into a single representative keyword or a consolidated keyword list at the theme level. This eliminates duplicate keyword processing across multiple groups while maintaining the same keyword coverage, directly reducing processing time without sacrificing mapping accuracy.
Solution Approach 2:
The patent performs preliminary theme-based keyword consolidation and deduplication during the data structure construction phase. By pre-processing keywords to identify and merge redundancies before the search campaign execution, the system reduces the computational burden during actual operations while ensuring accurate keyword-to-theme mappings are established in advance.
Data Source
AI summary
To automatically map keywords to landing pages, a system obtaining a dataset including an initial set of groups of content items, each of the groups mapped to a respective landing page in an initial set of landing pages, and each of the content items associated with one or more keywords in an initial keyword vocabulary; generates a reduced dataset based on the obtained dataset, which includes (i) generating a reduced set of landing pages based on the initial set of landing pages using parameters of links associated with the respective landing pages, (ii) clustering the keywords to determine a set of themes associated with the dataset, and (iii) generating a reduced set of groups, including identifying overlaps in themes included in the set of themes, between the groups. The system further uses the generated data structure to map a received search term to one or more of the content items.