Ryan Cooper

Paul Anderson photo

Sandia National Labs

Ryan is an undergraduate Student Intern at Sandia National Laboratories studying computer science at New Mexico Tech in Socorro, New Mexico.  He has a strong interest in Data Sciences and Machine Learning.  He has worked at Sandia for two years and has made significant and substantial contributions to enterprise search.


Ryan Cooper is speaking at the following session/s

Configuring Recommendations for Personalized Search at Sandia National Labs

Wednesday | 1:30PM - 2:10PM | Jefferson West

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.

Audience Takeaway
Learn how to configure personalization for enterprise search using Lucidworks Fusion out of the box with signals.

Intended Audience
Enterprise search managers, engineers, and other staff interested in introducing personalization based on signals into their search results.

All Levels