Product names are typically very long in ecommerce sites. for eg : “EYEBOGLER V-Neck Shawl Collar Stylish Men’s Solid T-Shirt”
the user might not even give the entire product names in the conversations. Also typos are expected too.
Option 1 : use lots of training examples for the model to learn. the problem is model might overfit to the programatically generated examples.
Option 2: use lookup tables to list down all product names. A regex match is done in this case. Here the problem is if we factor in the variations in which a user utters a product name (with only some parts of the name, with typos etc) the list can grow really big.
Which option is better to use and do we have any other way of solving this?