Artificial Intelligence for Constant Self-Tuning
Natural Language Processing (NLP)
Fusion employs NLP to detect phrases, topics and parts of speech for automatic classification and clustering of content in ways that make it easily accessible for search, browsing and predictive suggestions.
Signals Boosting
Fusion clusters users by behavior and elevates documents which perform best for each cluster’s favorite queries. Models then boost documents with features most relevant to a given cluster.
Head/Tail Query Rewriting
Head/Tail Analysis and automatic synonym detection analyzes the head (most common) and tail (infrequent) queries in the system, then auto-generates synonyms.
Query Intent Classification
Machine learning in Fusion uses training data on past searches and outcomes to predict the intent of each new query. Deep learning can use that intelligence for query parsing, query pipeline routing, autocomplete and type-ahead.
Learning to Rank
The Learning to Rank algorithm extracts tags such as product names, titles, and document categories to determine relevance scores in real-time.
Data Clustering and Classification
Fusion uses unsupervised machine learning to cluster similar content for pattern recognition. Classification, a supervised form of ML, teaches Fusion to classify new data to predetermined categories.