Personalized Search Ranking Algorithm Configuration
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Solution Overview
Problem
In heterogeneous enterprise environments, existing search systems fail to provide personalized and customizable ranking of search results, leading to irrelevant documents being prioritized over relevant ones, due to the lack of configurability and customization options for users.
Innovation Solution
A system that allows users to generate and update ranking algorithms based on customer settings, classifying ranking factors as default or custom, and query-dependent or static, enabling users to specify weights and attributes for personalized ranking of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search systems use fixed ranking algorithms, then the system structure remains simple, but the search results cannot be personalized and relevant documents are not prioritized
Solution Approach 1:
The patent segments the ranking algorithm into multiple independent ranking factors (e.g., relevance, recency, popularity, user preferences) that can be individually configured and weighted. This allows the system to offer personalized ranking without creating a monolithic complex algorithm, as each factor can be adjusted independently based on user needs.
Solution Approach 2:
The patent implements dynamic ranking algorithms that adapt based on user feedback, search history, and contextual information. The ranking factors and their weights are not fixed but can be modified in real-time, allowing the system to personalize results while maintaining manageable complexity through adaptive mechanisms.
2Adaptability or versatility
If the system provides extensive customization options for ranking, then user control and personalization improve, but the ease of operation deteriorates
Solution Approach 1:
The patent allows users to start with a small subset of ranking factors and gradually add more customization as needed. Users can begin with default rankings and selectively enable or adjust specific ranking factors, avoiding the need to configure all parameters at once, thus maintaining ease of operation while providing extensive customization capability.
Solution Approach 2:
The system provides pre-configured ranking profiles and default settings that work effectively for common search scenarios. Users can start with these pre-prepared configurations and only adjust parameters when specific needs arise, reducing the initial complexity burden while maintaining full customization potential.
3Measurement precision
If the system processes and ranks all documents uniformly, then the processing method remains simple, but the relevance of results to specific user needs decreases
Solution Approach 1:
The patent applies different ranking factors and weights to different document types, sources, or contexts. Instead of a single uniform ranking algorithm, the system can apply localized ranking strategies appropriate for specific document categories or user contexts, improving ranking accuracy while keeping individual algorithm components relatively simple.
Solution Approach 2:
The patent changes the parameters and weights of ranking factors based on document characteristics, user profiles, and search context. By dynamically adjusting parameters rather than using a fixed complex algorithm, the system achieves higher ranking precision for specific user needs while maintaining algorithmic manageability through parameter-based control.
Data Source
AI summary
Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations.