Post by account_disabled on Feb 27, 2024 13:53:17 GMT 4
The the Jaccard index of those two sets. The higher the score the more related they are. Perhaps we find that of the products with the tag ocean also have the tag sea we now know that the two are fairly wellrelated. However when we run the same measurement to compare basement or casement we find that they only have a Jaccard index of .. Even though they are very similar in terms of characters they mean quite different things. We can rule out mapping the two terms together.
Benefits The greatest benefit of using the Jaccard index is that it allows Kazakhstan Phone Number us to find highly related tags which may have absolutely no textual characteristics in common and are more likely to have an overly similar or duplicate results set. While most of the the metrics we have considered so far help us find good or bad tags the Jaccard index helps us find related tags without having to do any complex machine learning. Limitations While certainly useful the Jaccard index methodology has its own problems. do with tags that were used together nearly all the time but werent substitutes of one another. For example consider the tags babe ruth and his nickname sultan of swat.
The latter tag only occurred on products which also had the babe ruth tag since this was one of his nicknames so they had quite a high Jaccard index. However Google doesnt map these two terms together in search so we would prefer to keep the nickname and not simply redirect it to babe ruth. We needed to dig deeper if we were to determine when we should keep both tags or when we should redirect one to another. As a standalone this method also was not sufficient at identifying cases where a user consistently misspelled tags or used incorrect syntax as their products would essentially be orphans.
Benefits The greatest benefit of using the Jaccard index is that it allows Kazakhstan Phone Number us to find highly related tags which may have absolutely no textual characteristics in common and are more likely to have an overly similar or duplicate results set. While most of the the metrics we have considered so far help us find good or bad tags the Jaccard index helps us find related tags without having to do any complex machine learning. Limitations While certainly useful the Jaccard index methodology has its own problems. do with tags that were used together nearly all the time but werent substitutes of one another. For example consider the tags babe ruth and his nickname sultan of swat.
The latter tag only occurred on products which also had the babe ruth tag since this was one of his nicknames so they had quite a high Jaccard index. However Google doesnt map these two terms together in search so we would prefer to keep the nickname and not simply redirect it to babe ruth. We needed to dig deeper if we were to determine when we should keep both tags or when we should redirect one to another. As a standalone this method also was not sufficient at identifying cases where a user consistently misspelled tags or used incorrect syntax as their products would essentially be orphans.