6 Data Labeling Practices to Improve Search Relevance
Search relevance is a critical factor in improving brand recognition and user experience. Data labeling can help businesses achieve better search relevance by building and training machine learning models to deliver accurate results.
The blog post discusses six data labeling practices to improve search relevance:
- Evaluator training: Data annotators should be trained to work within strict guidelines and ensure high-quality data labeling.
- Query sampling: Query sampling helps minimize bias and ensures that the representative data is correct for the larger dataset.
- Data labeling project design: Data labeling projects should have a well-defined design workflow to break down the humongous task into smaller and simpler tasks.
- Extract diversified data: Data should be collected from various sources, ensuring diversity in demographics, languages, geographic regions, age groups, and search preferences.
- Dataset cleaning: Dataset cleaning is essential to fix incorrect, corrupted, and incorrectly formatted data. This will help to improve the accuracy of the AI model.
- Run pilot projects: Pilot projects are a great way to test the efficacy of the data labeling strategies and determine the time required to complete the entire project.
The blog post also highlights the importance of data labeling for search relevance and how it can help businesses improve product development and decision-making.
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