IBM Watson Discovery uses multiple artificial intelligence (AI) techniques to provide great out-of-the-box results for natural language queries. However, sometimes you might find that the results you’re getting aren’t as relevant as you’d like them to be and you need to improve them. Watson Discovery has a number of features that can help you tune and improve relevance, and here we present some ideas for how they can be used.

How does Watson Discovery optimize relevance

One thing to keep in mind when optimizing Watson Discovery is that it is built for “long-tail” use cases ( as explained here), that is, use cases where you have many varied questions and results that you can’t easily anticipate and optimize for.

Using the following methods helps Watson Discovery perform better across future, unseen queries, rather than optimize a specific result for a specific query. In cases where you have a few, frequently asked, very specific results, this might be best suited for training intents in Watson Assistant to recognize these important, foreseeable questions (the “big head” of the information needs distribution) and have well-defined responses. Often, real-world use cases involve a combination of foreseeable “big head” information needs and unforeseeable “long-tail” information needs. Those use cases can be served best by a combination of Watson Assistant and Watson Discovery (that is, using the Watson Assistant search skill or other common integrations).

You can read the full blog post on IBM Developer.

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Source: Artificial Intelligence on Medium