Time-Enriched Query Clustering for Stable Trending Topics
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Solution Overview
Problem
Conventional one-stage clustering techniques for search queries are ineffective in handling temporal variations and lexical imprecision, leading to unstable and inaccurate clustering of user search information, especially in dynamic environments like search engines.
Innovation Solution
A two-stage, time-enriched system for query clustering that includes a feature-based grouping stage followed by an unsupervised content-based clustering stage, utilizing time-window based voting and rolling average distances to stabilize clustering results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional one-stage clustering techniques are used for search queries, then the clustering process is simple and fast, but the clustering results are unstable and inaccurate due to temporal variations and lexical imprecision
Solution Approach 1:
The clustering process is divided into two distinct stages: (1) a feature-based grouping stage that performs initial clustering using lexical features, and (2) an unsupervised content-based clustering stage that refines groups using semantic content analysis. This segmentation allows each stage to specialize in different aspects of clustering, improving overall stability while managing complexity through modular design
Solution Approach 2:
The feature-based grouping stage performs preliminary clustering using readily available lexical features before the more computationally intensive content-based clustering stage. This preliminary action reduces the search space and provides a stable foundation for subsequent refinement, addressing temporal variations by establishing initial groupings that can be progressively refined
2Measurement precision
If conventional one-stage clustering techniques are used, then computational resources are conserved, but clustering precision is poor due to inability to handle temporal variations and lexical imprecision
Solution Approach 1:
The two-stage clustering process segments computational efforts: the first stage uses efficient feature-based grouping for initial clustering, and the second stage applies more resource-intensive content-based clustering only to refined groups. This segmentation improves clustering accuracy while controlling computational resource consumption by avoiding redundant processing
Solution Approach 2:
The content-based clustering stage applies semantic analysis selectively to groups that require refinement, rather than processing all queries uniformly. This partial action approach improves precision for critical cases while conserving computational resources on already well-clustered groups
3Stability of the object's composition
If time-window based voting and rolling average distances are used, then temporal fluctuations are reduced and clustering stability is improved, but the processing time and computational complexity increase
Solution Approach 1:
The feature-based grouping stage performs preliminary clustering using time-window based voting to establish stable initial groupings before content-based clustering. This preliminary action reduces temporal fluctuations early in the process, preventing propagation of instability to subsequent stages and reducing overall processing time
Data Source
AI summary
In an example, in connection with a search clustering system, a grouping component retrieves a timestamp set of news queries and determines a time-stable set of news query groups by performing the first stage of a two-stage clustering technique. A clustering component determines a time-stable set of news query groups clusters by performing the second stage of the two-stage clustering technique. The performance of the two-stage clustering technique is aided by a least recently used caching component. The time-stable set of news query groups clusters may be served to a web page in order to generate a trending topic list for display.