An effective machine learning search engine goes beyond simple search or AI techniques.
Rather than having humans manually boost and block individual items, a machine learning search engine can inspect user behavior to put the most relevant item near the top. When user behavior isn’t enough, the machine can be taught to recognize patterns in results and automatically boost documents that are better matches.
Collaborative filtering uses what users actually click on in response to a query and can boost or recommend items that are frequently clicked.
Going beyond manually created rules, teach the machine learning search engine the ideal order of results for a given query so the system can then learn to rank things properly.
Algorithmically evaluate your search rankings. How close was your ranking to ideal based on use behavior?
Two customers searching on the same terms might be looking for different things. If I’m in Washington state looking for a “shovel” in January I probably am looking for a different type of shovel than someone in Georgia during April. Just like when someone in sales looks for a “sales forecast” they might be looking for different data than someone in an executive role.
Machine learning techniques can understand these distinctions and make search more personal.
Identify customers or employees based on their interests and assign labels based on keywords.
Teach the machine learning model to recognize your existing content categories or departments and which content belongs there.
Understand why users didn’t find any results. Fix user queries when they misspell, misorder or misdescribe what they’re looking for.
If two queries differ by one word or phrase and the spelling of that phrase or term is similar, you probably have a misspelling. This can be fixed so that misspelled queries match the head queries that actually returned results.
Detect when two queries are alike and used in similar contexts, or contain synonyms. Evaluate which query is more effective in terms of click-through rates and replace one with the other.
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.
Lucidworks was named a leader in the 2022 Gartner Magic Quadrant for Insight Engines.
Forrester Total Economic Impact for Lucidworks 2023: $13.9M net present value, <6 months payback
of documents indexed for the world's biggest organizations
50% of the top U.S. online retailers use Lucidworks
