Search relevance is how questions are answered through search. It's the process of changing the ranking of search results for a user query to return what users want. A search for 'iPhone XS' should rank documents highly when the product name matches. But a different query, 'smartphone with two cameras' would require a completely different strategy for ranking candidate results. What gives teams a headache is that all the diverse use cases for search must be handled by a single ranking algorithm.
This is where Learning to Rank comes in. We will discuss how search can be treated as a machine learning problem. 'Learning to Rank' takes the step to returning optimized results to users based on patterns in usage behavior. We will talk through where Learning to Rank has shined, as well as the limitations of a machine learning-based solution to improve search relevance.
11:50 am - 12:00 pm Arrival and socializing
12:00 pm - 12:10 pm Opening
12:10 pm - 1:50 pm Ramzi Alqrainy, "Solr Learning to Rank: Non-Search as a Machine Learning Problem"
1:50 pm - 2:00 pm Q&A
Webinar ID: 831 6902 3352
Website: Click to Visit
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