Today we continue discussing the best practice from storage engineering:
493) To find similar people to form a group, we use some form of a similarity score. One way to calculate this score is to plot the items that the people have ranked in common and use them as axes in a chart. Then the people who are close together on the chart can form a group. These scores can then be used with tags. The same applies to resources.
494) To determine the closeness a couple of mathematical formula help. In this case, we could use the Euclidean distance or the Pearson co-efficient. The Euclidean distance finds the distance between two points in a multidimensional space by taking the sum of the square of the differences between the coordinates of the points and then calculating the square root of the result.
495) The Pearson correlation co-efficient is a measure of how highly correlated the two variables are. It’s generally a value between -1 and 1 where -1 means that there is a perfect inverse correlation and 1 means there is a perfect correlation while 0 means there is no correlation. It is computed with the numerator as the sum of the two variables taken together minus the average of their individual sums and this is divided by the square-root of the product of the squares of the substitutions to the numerator by using the same variable instead of the other.
496) Tags can be used to make recommendations against the data to be searched. Tags point to groups and the preferences of the group is used to make a ranked list of suggestions. This technique is called collaborative filtering. A common data structure that helps with keeping track of preferences is a nested dictionary. This dictionary could use a quantitative ranking say on a scale of 1 to 5 to denote the preferences of the participants in the selected group.
493) To find similar people to form a group, we use some form of a similarity score. One way to calculate this score is to plot the items that the people have ranked in common and use them as axes in a chart. Then the people who are close together on the chart can form a group. These scores can then be used with tags. The same applies to resources.
494) To determine the closeness a couple of mathematical formula help. In this case, we could use the Euclidean distance or the Pearson co-efficient. The Euclidean distance finds the distance between two points in a multidimensional space by taking the sum of the square of the differences between the coordinates of the points and then calculating the square root of the result.
495) The Pearson correlation co-efficient is a measure of how highly correlated the two variables are. It’s generally a value between -1 and 1 where -1 means that there is a perfect inverse correlation and 1 means there is a perfect correlation while 0 means there is no correlation. It is computed with the numerator as the sum of the two variables taken together minus the average of their individual sums and this is divided by the square-root of the product of the squares of the substitutions to the numerator by using the same variable instead of the other.
496) Tags can be used to make recommendations against the data to be searched. Tags point to groups and the preferences of the group is used to make a ranked list of suggestions. This technique is called collaborative filtering. A common data structure that helps with keeping track of preferences is a nested dictionary. This dictionary could use a quantitative ranking say on a scale of 1 to 5 to denote the preferences of the participants in the selected group.
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