How events improve search results automatically | Algolia

In the realm of AI search and discovery, events play a crucial role in enhancing accuracy and optimization. AI models rely heavily on high-quality event data to learn, make accurate predictions, and drive meaningful improvements. Algolia NeuralSearch utilizes machine learning models that convert terms into mathematical expressions called vector embeddings, enabling the understanding of search queries' meaning. Events help improve relevance by determining which fields best represent a record's meaning and their weighting. This process trains an 'expression' of fields and associated weightings, which is then used to 'vectorize' each record. The inclusion of variantFirmness in the example demonstrates the difficulty in training an optimal expression. Events can automatically determine which fields should be considered when training the expression and with what associated weighting. Algolia NeuralSearch continuously improves field weights over time, adapting to search trends and new data. Current customers can transition seamlessly to NeuralSearch if they have sufficient event data, while new customers need to set up events and generate enough data for optimal performance.