To perform keyword clustering, we will use the OpenAI API ( documentation here ). It will be us to set the model and parameters to be us. At this point, there are two approaches: using only 3.5 or also using.
Depending on how much we are willing to spend
In both cases, the operation is bas on batch clustering of keywords (50 at a time or 100 at a time, for example).
What is the reason for doing it this way? Any model has a finite context window (which can be seen here ). While it is true that new versions expand this context window and improve performance, the reality is that a deterioration in the quality obtain is observ as we increase the number of tokens us. That is, even if we could pass 10,000 keywords at once, the results would probably be very poor.
From here, and as
I just mention, we could use one or both fantuan phone number versions. Both techniques differ a little from each other: This is the cheapest approach but it has more flaws. In addition, it requires that you pass prefin categories in advance.
If you were to create
The new categories yourself, you would create many more than you should and some are unnecessary since they are includ in other categories (e.g. “green dress”, “summer dress” and “sleeveless dress” would go into the “dresses” category). The steps how to improve your ranking on meal delivery platforms? to follow are the following:
Step 1. The preparation:
We start from a set of keywords and a set of category groups prefin by us in advance. If it is a process that is launch every day, it should be taken into account that some of today’s keywords have already been classifi on another day and, therefore, it is not usa data necessary to classify them again (in addition, this way we avoid problems of consistency in the classification of the same keyword).