A method of generating a predictive model of categories of venues visited by a user is provided. The method may include 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.
Please help me write a proper abstract based on the patent claims. CLAIM: 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.