If I am shopping online for a shovel, there’s a big difference in my search results if I’m search for a garden shovel in the summer or a snow shovel in the winter. How does the search engine know what I mean?
Older search systems would use a big long list of manually inputted rules and logic to decide that if it’s winter time then show the user snow shovels and if it’s summer then maybe a gardening trowel or sand bucket and shovel for the beach. But when you have thousands or millions of products this can get unwieldy fast.
Query intent classification starts with a set of training data, which is a list of queries from users and important context like the user’s location and date it was when they clicked on a particular type of shovel. This data gets fed into your neural network for analysis and deep learning. Then the next time a similar user with a similar history and similar location starts a search, the system will automatically boost the intended results. This is one way neural networks help avoid hand-constructing rules, complex algorithms, potential human error, and overall headaches.
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