Adaptive Search Filter Recommendations Using Mixed ML Sources
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Solution Overview
Problem
Existing search engines provide static filtering options and suggested search terms that do not significantly reduce the amount of irrelevant results, requiring users to sift through numerous documents, files, and web pages, leading to inefficiency in finding relevant information.
Innovation Solution
A system that recommends search filter tiles based on a combination of machine learning-generated and non-machine learning-generated filters, adjusting the ratio of recommendations based on user search history, predefined rules, and confidence values, to optimize the filtering process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static filtering options are provided, then the search system is simple to implement, but the user must make several selections before initiating a desired search, reducing search efficiency
Solution Approach 1:
The system performs preliminary analysis of user search history and characteristics before the search is initiated, pre-generating personalized filter recommendations. This allows the system to present ready-made filter suggestions rather than requiring users to manually select from static options, thereby reducing the time spent on filter selection while maintaining ease of operation.
2Loss of information
If search suggestions are provided, then users receive word recommendations, but the number of documents, files, and web pages being searched stays the same
Solution Approach 1:
The system segments the search result set by applying multiple specialized filters simultaneously (e.g., file type, date range, author, relevance score) rather than presenting a single undifferentiated list. This segmentation allows users to quickly identify relevant information within specific categories, reducing the perceived quantity of irrelevant results while maintaining comprehensive search coverage.
3Measurement precision
If machine learning models generate search filter recommendations, then the relevance of recommendations improves, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary layer that translates complex machine learning model outputs into simple, user-friendly filter recommendations. The ML model analyzes user behavior patterns and generates probability scores, but an intermediary component converts these into actionable filter suggestions with confidence indicators, maintaining high accuracy while hiding the underlying complexity from users.
4Productivity
If dynamic filter recommendations are provided, then search efficiency improves, but the computational resources required increase
Solution Approach 1:
The system applies partial dynamic filtering by generating recommendations for only the most relevant filters based on user context, rather than dynamically adjusting all possible filters. This selective approach maintains high search efficiency for critical filters while reducing computational overhead by omitting dynamic analysis for less important filtering dimensions.
Data Source
AI summary
Techniques for generating and presenting search filter recommendations are disclosed. A system determines a composition of search filter tiles, including a relative number of machine learning-generated search filter tiles and non-machine learning-generated search filter tiles, based on recommendation criteria. The recommendation criteria include an amount of user search history data that exists. The system applies search filter recommendation policies to generate a set of search filter recommendations, including hard-coded search filters, search filter recommendations generated by a user-specific machine learning model, search filter recommendations generated by a machine learning model trained on data from multiple different users, and algorithm-based recommendations. The system generates different sets of search filter recommendations made up of different ratios of recommendations from different sources based on determining if search filter criteria are met.