Using Similarity for Grouping Fonts and Individuals for Recommendations
A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor to execute the instructions to perform operations that include receiving data representing a plurality of fonts, each font being different. For each pair of different fonts, operations include determining a level of similarity from a metric calculated for each font included in the pair, the metric being calculated from the received data and representing one or more font attributes. Operations also include grouping the fonts of the plurality of fonts based on the determined levels of similarities. Operations also include identifying individuals having an affinity towards one or more fonts included in the font groups. Operations also include grouping the identified individuals based on the affinity, and, presenting information to one or more of the identified individuals based on the grouping of the individuals.
1 . A computing device implemented method comprising:
receiving data representing a plurality of fonts, each font being different;
for each pair of different fonts, determining a level of similarity from a metric calculated for each font included in the pair, the metric being calculated from the received data and representing one or more font attributes;
grouping the fonts of the plurality of fonts based on the determined levels of similarities;
identifying individuals having an affinity towards one or more fonts included in the font groups;
grouping the identified individuals based on the affinity; and
presenting information to one or more of the identified individuals based on the grouping of the individuals.
2 . The computing device implemented method of claim 1 , wherein the received data is solicited from users.
3 . The computing device implemented method of claim 1 , wherein the received data is unsolicited from users.
4 . The computing device implemented method of claim 1 , wherein in the received data is weighted based upon one or more font attributes represented by the data.
5 . The computing device implemented method of claim 1 , wherein a portion of the received data is adjusted to identify commonality with another portion of the received data.
6 . The computing device implemented method of claim 1 , wherein the grouping the fonts utilizes a clustering algorithm.
7 . The computing device implemented method of claim 1 , wherein the metric for a font is a vector of numerical elements and each numerical element represents an attribute of the font.
8 . The computing device implemented method of claim 1 , wherein the level of similarity between two fonts is determined by calculating the distance between the metrics of the two fonts.
9 . The computing device implemented method of claim 1 , wherein presenting information to one or more of the identified individuals includes providing font selection recommendations to the group individuals.
10 . The computing device implemented method of claim 1 , wherein presenting information to one or more of the identified individuals includes providing related product or service recommendations to the group individuals.
11 . The computing device implemented method of claim 1 , further comprising:
repeating the level of similarity determinations to update the font groups.
12 . The computing device implemented method of claim 1 , further comprising:
repeating the identifying of individuals to update the groups of individuals.
13 . A system comprising:
a computing device comprising:
a memory configured to store instructions; and
a processor to execute the instructions to perform operations comprising:
receiving data representing a plurality of fonts, each font being different;
for each pair of different fonts, determining a level of similarity from a metric calculated for each font included in the pair, the metric being calculated from the received data and representing one or more font attributes;
grouping the fonts of the plurality of fonts based on the determined levels of similarities;
identifying individuals having an affinity towards one or more fonts included in the font groups;
grouping the identified individuals based on the affinity; and
presenting information to one or more of the identified individuals based on the grouping of the individuals.
14 . The system of claim 13 , wherein the received data is solicited from users.
15 . The system of claim 13 , wherein the received data is unsolicited from users.
16 . The system of claim 13 , wherein in the received data is weighted based upon one or more font attributes represented by the data.
17 . The system of claim 13 , wherein a portion of the received data is adjusted to identify commonality with another portion of the received data.
18 . The system of claim 13 , wherein the grouping the fonts utilizes a clustering algorithm.
19 . The system of claim 13 , wherein the metric for a font is a vector of numerical elements and each numerical element represents an attribute of the font.
20 . The system of claim 13 , wherein the level of similarity between two fonts is determined by calculating the distance between the metrics of the two fonts.
21 . The system of claim 13 , wherein presenting information to one or more of the identified individuals includes providing font selection recommendations to the group individuals.
22 . The system of claim 13 , wherein presenting information to one or more of the identified individuals includes providing related product or service recommendations to the group individuals.
23 . The system of claim 13 , the operations further comprising:
repeating the level of similarity determinations to update the font groups.
24 . The system of claim 13 , the operations further comprising:
repeating the identifying of individuals to update the groups of individuals.
25 . One or more computer readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising:
receiving data representing a plurality of fonts, each font being different;
for each pair of different fonts, determining a level of similarity from a metric calculated for each font included in the pair, the metric being calculated from the received data and representing one or more font attributes;
grouping the fonts of the plurality of fonts based on the determined levels of similarities;
identifying individuals having an affinity towards one or more fonts included in the font groups;
grouping the identified individuals based on the affinity; and
presenting information to one or more of the identified individuals based on the grouping of the individuals.
26 . The computer readable media of claim 25 , wherein the received data is solicited from users.
27 . The computer readable media of claim 25 , wherein the received data is unsolicited from users.
28 . The computer readable media of claim 25 , wherein in the received data is weighted based upon one or more font attributes represented by the data.
29 . The computer readable media of claim 25 , wherein a portion of the received data is adjusted to identify commonality with another portion of the received data.
30 . The computer readable media of claim 25 , wherein the grouping the fonts utilizes a clustering algorithm.
31 . The computer readable media of claim 25 , wherein the metric for a font is a vector of numerical elements and each numerical element represents an attribute of the font.
32 . The computer readable media of claim 25 , wherein the level of similarity between two fonts is determined by calculating the distance between the metrics of the two fonts.
33 . The computer readable media of claim 25 , wherein presenting information to one or more of the identified individuals includes providing font selection recommendations to the group individuals.
34 . The computer readable media of claim 25 , wherein presenting information to one or more of the identified individuals includes providing related product or service recommendations to the group individuals.
35 . The computer readable media of claim 25 , the operations further comprising:
repeating the level of similarity determinations to update the font groups.
36 . The computer readable media of claim 25 , the operations further comprising:
repeating the identifying of individuals to update the groups of individuals.