Sandia National Labs
Ryan Cooper is speaking at the following session/s
Configuring Recommendations for Personalized Search at Sandia National Labs
Learn how Sandia configured personalized search for enterprise search users in days (not weeks, or even years) from data gathering and model building to query configuration. In enterprise search, the assumed preference of each user is the number of times that they have previously clicked on pages (an observed weight) that is then used to "co-cluster" with other users to make predictions about what pages they will find useful. Recommender systems, colloquially RecSys, are a subclass of information filtering systems that seek to predict the rating or preference a user would give to an item. Alternating Least Squares (ALS) matrix factorization is a common technique in collaborative filtering and has been proven to be an effective solution to the co-clustering problem, where the primary model for recommendations must be trained through the “ALS Recommender” job. Explore Lucidworks Fusion out-of-the-box RecSys tools for a multitude of applications.
Learn how to configure personalization for enterprise search using Lucidworks Fusion out of the box with signals.
Enterprise search managers, engineers, and other staff interested in introducing personalization based on signals into their search results.