Diversity Group Search Ranking Using Multiple Scoring Functions
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Solution Overview
Problem
Conventional search engines often fail to return relevant results that do not fit the prioritized characteristics of their algorithms, leading to missed important information such as entities or locations, as they primarily rely on content relevance without considering social context or information.
Innovation Solution
Implementing a diversity scoring system that applies multiple scoring functions to search results, organizing them into groups based on different criteria, and selecting top results from each group to provide a diverse and optimized set of search results to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a search algorithm prioritizes users of the social network based on social context, then social relevance is improved, but other types of results such as entities or locations are lost
Solution Approach 1:
The search results are divided into multiple diversity groups based on different scoring functions. Each group focuses on specific characteristics (e.g., social context, content relevance, entity type), allowing the system to segment results by type while maintaining overall diversity in the final presentation to users.
2Device complexity
If a single scoring function is used to rank search results, then the algorithm is simple, but diverse types of information are not captured
Solution Approach 1:
Multiple scoring functions are applied to the same set of search results, with each function serving a different purpose (social context evaluation, content relevance assessment, entity type identification). This multi-functional approach allows a single search system to handle diverse information types effectively.
Solution Approach 2:
Different scoring functions evaluate search results using different parameters and weighting schemes. By changing the evaluation parameters across multiple functions and then aggregating results, the system captures diverse information types without requiring completely separate search algorithms for each type.
3Adaptability or versatility
If multiple scoring functions are applied to search results, then diverse results are captured, but computational complexity increases
Solution Approach 1:
The computational workload is segmented across multiple scoring functions that operate in parallel on different aspects of the search results. Each function handles a specific evaluation dimension, distributing the computational complexity rather than concentrating it in a single complex algorithm.
Solution Approach 2:
The system discards intermediate results from individual scoring functions that do not meet thresholds, while recovering and preserving top results from each function. This selective retention strategy reduces the need to process and compare all intermediate results from every scoring function, lowering overall computational complexity.
Data Source
AI summary
In one embodiment, a method includes receiving a plurality of search results based on a search query from a user. A computing system determines a plurality of scores for each search result, each score generated by applying a distinct scoring function of a plurality of scoring functions to the search result. The computing system generates a plurality of diversity groups, each diversity group corresponding to a scoring function of the plurality of scoring functions, each diversity group including at least a subset of the plurality of search results ordered according to the scores generated by applying the scoring function to the at least the subset of the plurality of search results. The method further includes selecting at least one of the plurality of search results from each diversity group and sending the selected search results to the user.