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> Knowing what the most common questions are doesn't help you find the corresponding answers. At best you can find the result that users click on most frequentl
by pizza234 7y ago
> Knowing what the most common questions are doesn't help you find the corresponding answers. At best you can find the result that users click on most frequently
I doubt that users have a strict expectation of finding the correct (interpretation of "corresponding") answer. Even if the most clicked answer[s] are still not correct, they may still be the ones who suck less, which can still be acceptable/desirable.
Keep in mind that in this perspective, the concept is similar to Google Translate - Google built a translator that, at least originally, doesn't understand language, instead, it applies (applied) a static model on a large amount of documents. While they certainly poured a large amount of money on it, it's success can't be centered purely on the economical factor.
- mantap 7y agoWell in the given example the user asks a question "who was the actor that..." and Google gives them the answer. This is really hard to achieve by accident no matter how much traffic you get. It requires NLP machinery to answer a question, it's not a matching problem but an understanding problem. There are at least three kinds of queries that a search engine has to handle. Requests for websites e.g. "Facebook". Traditional keyword searches across the web e.g. "Twitter ban political ads", and questions "Who was the guy who voiced bender?".