Trend-Based Distribution Parameter Suggestion for Content Campaigns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advertisers face challenges in identifying and adapting to popular search queries in real-time, leading to missed opportunities for presenting relevant content items to users, as existing content distribution systems struggle to manage a large number of distribution keywords and predict changing user interests effectively.
Innovation Solution
A method that suggests additional distribution parameters to content sponsors based on changes in search query volume and similarity between content distribution campaigns, using trend scores to identify increasing user interest and recommending relevant search queries as additional distribution keywords, thereby enhancing the relevance of content items presented to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content distribution systems manually manage distribution keywords, then advertisers can control content placement, but advertisers cannot identify and adapt to popular search queries in real-time
Solution Approach 1:
The system automatically monitors search query volumes, calculates trend scores, and generates distribution parameter suggestions without requiring advertiser intervention. The system serves itself by autonomously identifying trending queries and notifying advertisers, eliminating the time loss associated with manual monitoring and adaptation.
Solution Approach 2:
The system continuously monitors search query data and provides feedback to advertisers through trend score calculations and suggestions. This feedback loop enables advertisers to adapt to popular search queries in real-time by receiving automated notifications about trending parameters, resolving the contradiction between adaptability and time loss.
2Adaptability or versatility
If advertisers use many distribution keywords to cover various search queries, then content relevance improves, but management complexity increases
Solution Approach 1:
The system acts as an intermediary between search query data and advertisers, automatically analyzing trends and generating suggestions. This intermediary function allows advertisers to benefit from comprehensive keyword coverage without directly managing the complexity of selecting and monitoring numerous distribution parameters.
Solution Approach 2:
The system autonomously performs the complex task of analyzing search query volumes, calculating trend scores, and identifying relevant distribution parameters. By automating this process, the system enables comprehensive query coverage while eliminating the management burden from advertisers.
3Reliability
If content distribution systems use existing distribution parameters, then content placement is stable, but timing of content presentation misses changing user interests
Solution Approach 1:
The system dynamically adjusts distribution parameter suggestions based on real-time search query trend analysis. By continuously monitoring changes in search volumes and calculating trend scores, the system adapts content placement to changing user interests while maintaining the stability of the overall distribution framework through automated, data-driven recommendations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content are disclosed. In one aspect, a method includes accessing data specifying a plurality of search queries. Content distribution campaigns (“campaigns”) in which distribution of at least one content item is conditioned on a distribution parameter matching one of the search queries are identified. Two or more similar campaigns are identified, and a search query that matches a distribution parameter in at least one of the similar campaigns is identified as a candidate content distribution parameter. A trend score for the candidate content distribution parameter is determined based on a change in a submission rate of search queries that match the candidate distribution parameter. Suggestion data suggesting the candidate content distribution parameter as an additional content distribution parameter for at least one of the similar campaigns is provided based on the trend score.