Many organizations have deployed human-in-the-loop (HITL) to help advance their products, features, and search algorithms. This process involves collecting inputs and measurements from humans to train, measure, and optimize the relevance and quality of results for end users and to create a better user experience.
Improving and training search algorithms relies on many relevance signals and requires vast amounts of training data. This is especially true when products are international and support many languages and markets. These human evaluators label, annotate, or rate high volumes of search queries with their corresponding results to generate meaningful insights that help improve search algorithms' relevance standards and quality.
DataForce manages this type of work on a secure platform using a vetted community of human evaluators.
Besides search relevance, our service expands to ads relevance, recommendation relevance, and other types of content moderation.