METHOD AND SYSTEM FOR INFERRING USER VISIT BEHAVIOR OF A USER BASED ON SOCIAL MEDIA CONTENT POSTED ONLINE

1 . A method of generating a predictive model of categories of venues visited by a user, the method comprising:

extracting a first content feature from a first digital post to an online social media platform selected from a plurality of digital posts;

extracting a second content feature from a second digital post to an online social media platform selected from the plurality of social media posts;

aggregating the first and second content features;

inferring at least one of a frequency and a regularity of visits to a venue category associated with the plurality of digital posts based on the aggregated first and second content features using a neural network; and

determining at least one of a frequently visited venue category and a regularly visited venue category based on the inferred frequency and a regularity of visits associated with the plurality of digital posts.

2 . The method of claim 1 , further comprising automatically generating a digital communication, and sending the digital communication to a first user associated with the plurality of digital posts to the online social media platform based on the determined one of a frequently visited venue category and a regularly visited venue category.

3 . The method of claim 1 , wherein the extracting the first content feature from the first digital post comprises extracting at least one of a first visual content feature and a first textual content feature from the first digital post; and

wherein the extracting the second content feature from the second digital post comprises extracting at least one of a second visual content feature and a second textual content feature from the second digital post.

4 . The method of claim 1 , wherein the extracting the first content feature from the first digital post comprises:

extracting both a first visual content feature and a first textual content feature from the first digital post; and

integrating the first visual content feature and the first textual content feature to generate a first integrated content feature;

wherein the extracting the second content feature from the second digital post comprises:

extracting both a second visual content feature and a second textual content feature from the second digital post; and

integrating the second visual content feature and the second textual content feature to generate a second integrated content feature; and

wherein aggregating the first content feature and the second content feature together comprises aggregating the first integrated content feature and the second integrated content feature.

5 . The method of claim 1 , further comprising training the neural network by:

extracting a content feature from each of a plurality of digital posts to an online social media platform associated with a plurality of users;

extracting metadata associated with each of the plurality of digital posts;

determining a venue category associated with each digital post based on the extracted metadata; and

optimizing one or more parameters of a predictor model based on an association between the determined venue category and the extracted content features; and

wherein the inferring at least one of a frequency and a regularity of visits to a venue category comprises inferring venue categories based on the aggregated first and second content features using the optimized predictor model.

6 . The method of claim 5 , wherein the extracted metadata comprises one or more of:

Global Positioning System (GPS) data, geotag data, and check-in data associated with each digital post.

7 . The method of claim 1 , further comprising sorting the plurality of digital posts into a first group of digital posts and a second group of digital post based on temporal data associated with each digital post; and

wherein the inferring at least one of a frequency and a regularity of visits to a venue category comprises:

inferring a first at least one of a frequency and a regularity of visits to a venue category associated with the first group of digital posts; and

inferring a second at least one of a frequency and a regularity of visits to a venue category associated with the second group of digital post.

8 . A non-transitory computer readable medium having stored therein a program for making a computer execute a method of generating a predictive model of categories of venues visited by a user, the method comprising:

extracting a first content feature from a first digital post to an online social media platform selected from a plurality of digital posts;

extracting a second content feature from a second digital post to an online social media platform selected from the plurality of social media posts;

aggregating the first and second content features;

inferring at least one of a frequency and a regularity of visits to a venue category associated with the plurality of digital posts based on the aggregated first and second content features using a neural network; and

determining at least one of a frequently visited venue category and a regularly visited venue category based on the inferred frequency and a regularity of visits associated with the plurality of digital posts.

9 . The non-transitory computer readable medium of claim 8 , further comprising automatically generating a digital communication, and sending the digital communication to a first user associated with the plurality of digital posts to the online social media platform based on the determined one of a frequently visited venue category and a regularly visited venue category.

10 . The non-transitory computer readable medium of claim 8 , wherein the extracting the first content feature from the first digital post comprises extracting at least one of a first visual content feature and a first textual content feature from the first digital post; and

wherein the extracting the second content feature from the second digital post comprises extracting at least one of a second visual content feature and a second textual content feature from the second digital post.

11 . The non-transitory computer readable medium of claim 8 , wherein the extracting the first content feature from the first digital post comprises:

extracting both a first visual content feature and a first textual content feature from the first digital post; and

integrating the first visual content feature and the first textual content feature to generate a first integrated content feature;

wherein the extracting the second content feature from the second digital post comprises:

extracting both a second visual content feature and a second textual content feature from the second digital post; and

integrating the second visual content feature and the second textual content feature to generate a second integrated content feature; and

wherein aggregating the first content feature and the second content feature together comprises aggregating the first integrated content feature and the second integrated content feature.

12 . The non-transitory computer readable medium of claim 8 , further comprising training the neural network by:

extracting a content feature from each of a plurality of digital posts to an online social media platform associated with a plurality of users;

extracting metadata associated with each of the plurality of digital posts;

determining a venue category associated with each digital post based on the extracted metadata; and

optimizing one or more parameters of a predictor model based on an association between the determined venue category and the extracted content features; and

wherein the inferring at least one of a frequency and a regularity of visits to a venue category comprises inferring venue categories based on the aggregated first and second content features using the optimized predictor model.

13 . The non-transitory computer readable medium of claim 12 , wherein the extracted metadata comprises one or more of: Global Positioning System (GPS) data, geotag data, and check-in data associated with each digital post.

14 . The non-transitory computer readable medium of claim 8 , further comprising sorting the plurality of digital posts into a first group of digital posts and a second group of digital post based on temporal data associated with each digital post; and

wherein the inferring at least one of a frequency and a regularity of visits to a venue category comprises:

inferring a first at least one of a frequency and a regularity of visits to a venue category associated with the first group of digital posts; and

inferring a second at least one of a frequency and a regularity of visits to a venue category associated with the second group of digital post.

15 . A server apparatus comprising:

a memory storing digital content posted to an online social media platform comprising a plurality of digital posts associated with a first user;

a processor executing a process comprising:

extracting a first content feature from a first digital post to an online social media platform selected from a plurality of digital posts;

extracting a second content feature from a second digital post to an online social media platform selected from the plurality of social media posts;

aggregating the first and second content features;

inferring at least one of a frequency and a regularity of visits to a venue category associated with the plurality of digital posts based on the aggregated first and second content features using a neural network; and

determining at least one of a frequently visited venue category and a regularly visited venue category based on the inferred frequency and a regularity of visits associated with the plurality of digital posts.

16 . The server apparatus of claim 15 , wherein the process further comprises automatically generating a digital communication, and sending the digital communication to a first user associated with the plurality of digital posts to the online social media platform based on the determined one of a frequently visited venue category and a regularly visited venue category.

17 . The server apparatus of claim 15 , wherein the extracting the first content feature from the first digital post comprises extracting at least one of a first visual content feature and a first textual content feature from the first digital post; and

wherein the extracting the second content feature from the second digital post comprises extracting at least one of a second visual content feature and a second textual content feature from the second digital post.

18 . The server apparatus of claim 15 , wherein the extracting the first content feature from the first digital post comprises:

extracting both a first visual content feature and a first textual content feature from the first digital post; and

integrating the first visual content feature and the first textual content feature to generate a first integrated content feature;

wherein the extracting the second content feature from the second digital post comprises:

extracting both a second visual content feature and a second textual content feature from the second digital post; and

integrating the second visual content feature and the second textual content feature to generate a second integrated content feature; and

wherein aggregating the first content feature and the second content feature together comprises aggregating the first integrated content feature and the second integrated content feature.

19 . The server apparatus of claim 15 , wherein the process further comprises training the convolutional neural network by:

extracting a content feature from each of a plurality of digital posts to an online social media platform associated with a plurality of users;

extracting metadata associated with each of the plurality of digital posts;

determining a venue category associated with each digital post based on the extracted metadata; and

optimizing one or more parameters of a predictor model based on an association between the determined venue category and the extracted content features; and

wherein the inferring at least one of a frequency and a regularity of visits to a venue category comprises inferring venue categories based on the aggregated first and second content features using the optimized predictor model.

20 . The server apparatus of claim 15 , wherein the process further comprises sorting the plurality of digital posts into a first group of digital posts and a second group of digital post based on temporal data associated with each digital post; and

wherein the inferring at least one of a frequency and a regularity of visits to a venue category comprises:

inferring a first at least one of a frequency and a regularity of visits to a venue category associated with the first group of digital posts; and

inferring a second at least one of a frequency and a regularity of visits to a venue category associated with the second group of digital post.