Artificial Intelligence on Query
Automatic Query Rewriting
Query rewriting uses AI-generated data to improve incoming queries prior to submitting them to Fusion. These rewrites learn to map less common long-tail queries to more well-known query interpretations, producing more personalized search results with higher user click-through rates.
A/B Testing
Fusion’s Experiments API lets users set up relevancy experiments to optimize click-through rates and other relevancy metrics. Experiments can be run as A/B split tests on live user traffic, as Multi-armed Bandits tests that auto-adjust test parameters based on real-time feedback loops, or as offline backtesting simulations against historical clickstream data. This enables orders of magnitude more tests to be run in the same amount of time — without negatively impacting live customers.
Intent Detection
Fusion interprets query intent using proprietary phrase detection, synonym detection, misspelling detection, head-tail analysis, and concept understanding algorithms. Fusion’s Semantic Knowledge Graph relates these domain-specific entities (people, places, things, topics, phrases) and concepts within queries enabling contextual, semantic search that correctly interprets each user’s query within their unique context and returns the best search results.
Signal Analytics
User signals are collected for analysis on how to personalize the search experience. While signals are used to generate personalization profiles and recommendations for each user, they are also leveraged in aggregate to power boosting of most popular items and for learning to rank. Fusion also generates rich visualizations and reports from raw and aggregated signals to make business analysis of your search engine utilization fast and intuitive.