Search Result Ranking Using Feature-Based Relevance Functions
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Solution Overview
Problem
Conventional search systems struggle to effectively rank search results from community discussions due to the lack of rich mark-up in Usenet postings and differences in topological relationships, making techniques like PageRank analysis and anchor text unusable.
Innovation Solution
A ranking system that employs feature-based relevance functions, tailored to users and applications, using scoped information, digital artifact author attributes, and relationships between features, with the ability to generate and update relevance functions dynamically based on user feedback, to rank search results from digital artifact repositories such as Usenets, mailing lists, and archived discussions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search techniques (PageRank, anchor text) are used, then web page search effectiveness is improved, but they become unusable for community discussions due to lack of rich mark-up and different topological relationships
Solution Approach 1:
The patent applies local quality by creating domain-specific relevance functions tailored to community discussions. Instead of using a universal search algorithm, the system develops specialized relevance functions that incorporate features specific to discussion forums (thread structure, user profiles, posting patterns), making the search effective for this particular domain while acknowledging that other domains may require different approaches.
Solution Approach 2:
The patent changes parameters by transitioning from link-based metrics (PageRank, anchor text) to content-based and context-based features. The system evaluates features such as thread depth, user reputation, temporal patterns, and lexical similarity, fundamentally changing the parameter set used for ranking from structural link analysis to content and context analysis suitable for community discussions.
2Measurement precision
If feature-based relevance functions are implemented, then search result ranking accuracy is improved, but system complexity increases due to multiple features and dynamic function generation
Solution Approach 1:
The patent applies dynamics by making the relevance function adaptive and learnable. The system dynamically adjusts relevance function parameters based on user feedback and interaction patterns. The relevance function evolves over time, learning from user behavior to improve ranking accuracy without requiring manual reconfiguration of each feature weight, thus managing complexity through adaptive learning rather than static complex configurations.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with search results (clicks, dwell time, subsequent searches) are fed back into the system to refine relevance functions. This feedback loop allows the system to automatically adjust and improve ranking accuracy over time, reducing the need for manual tuning and making the complex system self-optimizing rather than requiring constant human intervention.
3Measurement precision
If comprehensive features (lexical, author attributes, repository attributes) are analyzed, then relevance determination accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing certain features such as user profiles, thread statistics, and repository metadata. These pre-computed features are cached and reused across multiple searches, avoiding redundant calculations. For example, user reputation scores and thread depth metrics are calculated once and stored, then quickly retrieved during search operations rather than recomputed each time, significantly reducing processing time while maintaining comprehensive feature analysis.
Data Source
AI summary
The present invention provides systems and methods that rank search results. Such ranking typically includes determining a relevance of individual search results via one or more feature-based relevance functions. These functions can be tailored to users and/or applications, and typically are based on scoped information (e.g., lexical), digital artifact author related attributes, digital artifact source repository attributes, and/or relationships between features, for example. In addition, relevance functions can be generated via training sets (e.g., machine learning) or initial guesses that are iteratively refined over time. Upon determining relevance, search results can be ordered with respect to one another, based on respective relevances. Additionally, thresholding can be utilized to mitigate returning results likely to be non-relevant to the query, user and/or application.