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Submitted by alexdenipaul on Mon, 10/12/2009 - 20:51.
So algorithms don't often give you things that users really understand. So we really payday loans, really wanted to deliver things that it made sense to the users. And then on the second level, we really wanted to understand everything about just a query standalone as much as possible. And this is to balance the whole, "Okay. What are the top things people care about in a whole category?" If I've got this bag of categories that users care about, now how do I pick the right ones that only apply to this query? And that is why we had an approach at a category level and also at our query level consolidate debt. Lastly, we did a lot of work around determining if we know that a query is in a category, is that actually the primary intent for that query. So, I don't know, like traffic may be a movie, but a lot of users when they type in traffic, they actually are just looking for how bad is the traffic right now. And that's an example of a query, even though belonging to a category, it may be an obscure intent.
Lastly, we have our ranking model. And our ranking model basically takes all of the different inputs at the category level and at the query level in order to do some modeling around what are the top intents that apply to our re-query. And, of course loan, we have a very tight feedback loop system from what are the things that users engage with to feed back into the ranking of the categories as well as discovering new ones.
Brady Forrest: And how fast do you have to make this calculation for each query?
Sanaz Ahari: I mean it's all pretty fast because we are scaling through millions of queries. So there's a combination of things for performance optimizations, we do some things offline and we do some things online. For things that don't change a lot and it makes sense for us to do it offline, we try to optimize it. But it's definitely a combination of the two. And our goal is with users credit cards, performance is just an expectation. So that's something that we can't compromise on. So everything happens in a matter of milliseconds basically for all of our computations.
Brady Forrest: And how much are you able to cache in case suddenly a query starts to trend up?
Sanaz Ahari: Right. For a lot of our headquarters, we definitely do a lot of caching, et cetera. And for real time spiky things, we have invested in an entire different system where we're constantly monitoring for spiky trends. So it's basically the two systems are basically kind of optimized both individually so that we always are aware of what are the things that are all of the sudden spiking a lot. And then being smart about the things that have already been -- you know, that are head queries, that people are re-querying for.
So algorithms don't often give you things that users really understand. So we really payday loans, really wanted to deliver things that it made sense to the users. And then on the second level, we really wanted to understand everything about just a query standalone as much as possible. And this is to balance the whole, "Okay. What are the top things people care about in a whole category?" If I've got this bag of categories that users care about, now how do I pick the right ones that only apply to this query? And that is why we had an approach at a category level and also at our query level consolidate debt. Lastly, we did a lot of work around determining if we know that a query is in a category, is that actually the primary intent for that query. So, I don't know, like traffic may be a movie, but a lot of users when they type in traffic, they actually are just looking for how bad is the traffic right now. And that's an example of a query, even though belonging to a category, it may be an obscure intent.
Lastly, we have our ranking model. And our ranking model basically takes all of the different inputs at the category level and at the query level in order to do some modeling around what are the top intents that apply to our re-query. And, of course loan, we have a very tight feedback loop system from what are the things that users engage with to feed back into the ranking of the categories as well as discovering new ones.
Brady Forrest: And how fast do you have to make this calculation for each query?
Sanaz Ahari: I mean it's all pretty fast because we are scaling through millions of queries. So there's a combination of things for performance optimizations, we do some things offline and we do some things online. For things that don't change a lot and it makes sense for us to do it offline, we try to optimize it. But it's definitely a combination of the two. And our goal is with users credit cards, performance is just an expectation. So that's something that we can't compromise on. So everything happens in a matter of milliseconds basically for all of our computations.
Brady Forrest: And how much are you able to cache in case suddenly a query starts to trend up?
Sanaz Ahari: Right. For a lot of our headquarters, we definitely do a lot of caching, et cetera. And for real time spiky things, we have invested in an entire different system where we're constantly monitoring for spiky trends. So it's basically the two systems are basically kind of optimized both individually so that we always are aware of what are the things that are all of the sudden spiking a lot. And then being smart about the things that have already been -- you know, that are head queries, that people are re-querying for.