How Does Fusion Hyper-Personalize?
Lucidworks can precisely personalize queries using signals from:
- Signal-driven relevance based on user behavior and queries
- Clustering and classification for a better search experience
- Modern distributed architecture built on Apache Solr and Spark
- Query rewriting algorithms like synonym detection, phrase detection, head tail detection, misspelling detection
- Recommenders with collaborative filtering, matrix decomposition, item/item and user/item recommenders
Learn more about how Lucidworks uses data to hyper-personalize every customer experience.
What to Read Next
Elements of AI-Powered Search
Visit the Lucidworks AI Hub for more information about Generative AI and Search.
Augmented Intelligence
There’s nothing artificial about intelligence. Augmented intelligence is when AI extends human judgment instead of replacing it.
Machine Learning
Machine learning and search engines are a incredible combination for creating powerful experiences for customers and employees.
Clustering & Classification
How clustering and classification algorithms can improve the search experience for your employees and customers.
Query Analysis
Underperforming queries aggravate everyone. Head/tail analysis stops it.
Signal Capture & User Behavior
Users are constantly telling you what they like and what they don’t. Are you listening? Predict user intent by applying signals.
Indexing
The best search applications index all of a company’s data so users have one unified search experience.
Hyper-Personalization
Personalization is about addressing people by name. Hyper-personalization is figuring out what they really want.
Natural Language Search
What if we could talk to computers in the same way we talk to people. Natural language search gets you there.