The article discusses the importance of conducting experiments in data science, especially in improving job board efficiency at IntelyCare. The author highlights a sort-by-relevance feature that enhances user experience but reveals complexities in defining 'relevance' as it relates to a scoring system rather than individual job-seeker needs. The article emphasizes the experimental nature of improving job listings, acknowledging the trade-offs between quantity and quality, while also noting the significant influence of Google traffic in shaping user interactions with the job board.
Our sort-by-relevance feature acts as the best lever for enhancing user experience and improving job board efficiency by promoting higher quality listings, despite trade-offs.
We score each job between 0 and 100 for relevance, and the sorting reflects that, emphasizing efficiency in steering users towards higher-quality job opportunities.
Although our system uses a relevance score, it diverges from traditional meaning; it's based on performance metrics relative to Google traffic rather than individual user relevance.
The challenge in such experiments is balancing between listing quantity and quality; achieving an optimal experience requires constant experimentation and data-driven adjustments.
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