Improving search relevance of Azure Cognitive Search by Bayesian optimization
This work addresses the challenge for ACS users in enhancing search result relevance, though it appears incremental as it applies Bayesian optimization to hyperparameter tuning.
The paper tackles the problem of improving search relevance in Azure Cognitive Search for specific usecases like product or document search, resulting in significant improvements in real-world metrics such as click-through rates and call-to-action rates.
Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a Service" cloud products in recent years. However, one of the major challenges for ACS users is to improve the relevance of the search results for their specific usecases. In this paper, we propose a novel method to find the optimal ACS configuration that maximizes search relevance for a specific usecase (product search, document search...) The proposed solution improves key online marketplace metrics such as click through rates (CTR) by formulating the search relevance problem as hyperparameter tuning. We have observed significant improvements in real-world search call to action (CTA) rate in multiple marketplaces by introducing optimized weights generated from the proposed approach.