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• US 10,204,170 B2
• Filed: 08/14/2015
• Issued: 02/12/2019
• Est. Priority Date: 12/21/2012
• Status: Active Grant

• Current Assignee: HighSpot, Inc.
• Original Assignee: HighSpot, Inc.
• Inventors: Viola, Paul, Sharp, Oliver, Wortendyke, David, Christensen, Erik, Wahbe, Robert, Dire, Nate, Millet, Steven
• Primary Examiner(s): Misir, Dave
• Application Number:
  US14/827,149
• Publication Number:
  US 20160048764A1
• Time in Patent Office: 1,278 Days
• Field of Search: 706 11
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

Systems and methods for selecting items of interest for an organization from a set of feeds, based on the interests that users have demonstrated through their interactions with existing content, are described herein. In some embodiments, the system is part of a content management service that allows users to add and organize files, media, links, and other information. The content can be uploaded from a computer, imported from cloud file systems, added via links, or pulled from various kinds of feeds.

Claims

1. A computer-implemented method of identifying information of interest within an organization, the method comprising:

determining use data that characterizes relationships among a plurality of information items;

for a plurality of users associated with the organization,

training at least one classifier based on the plurality of users associated with the organization, wherein the training comprises;

selecting a proper subset of the plurality of information items;

for each of the selected information items,

receiving, from a specific user associated with the organization, feedback indicating at least one preference of the specific user related to the selected information item, and

for each of a plurality of tokens associated with the selected information item,

 

attributing a score to the token based at least in part on the received feedback, and

 

storing the attributed score;

receiving an indication of a first group of two or more information items within the organization, wherein the first group of two or more information items within the organization includes at least two information items not included in the selected proper subset of the plurality of information items; and

for each of a plurality of information items of the first group of two or more information items within the organization,

for each of the specific user of the plurality of users associated with the organization,

accessing the at least one trained classifier that has been trained using feedback received from the specific user;

determining whether the specific user is likely to find the information item interesting based at least in part on the accessed at least one trained classifier, and

providing, for display, an indication of at least one information item of the first group of two or more information items within the organization that the specific user is likely to find interesting.

2. The method of claim 1, wherein the information items include user data and collections of information items.

3. The method of claim 2, wherein the organization includes a structured body of users with associated roles within the organization and who have access to the information items within the organization.

4. The method of claim 1, wherein determining whether a first user is likely to find a first information item interesting comprises:

identifying a plurality of tokens associated with the first information item,

for each of the identified plurality of tokens associated with the first information item, attributing a score to the identified token based on the at least one trained classifier; and

determining a score for the first information item based at least in part on the scores attributed to the identified plurality of tokens.

5. The method of claim 4, further comprising:

determining whether the determined score for the first information item exceeds a first predetermined threshold.

6. The method of claim 4, wherein the plurality of identified tokens include at least one keyword associated with the first information item, at least one category associated with the first information item, at least one title word associated with the first information item, and at least one word in the body of the first information item.

7. The method of claim 1, further comprising:

collaboratively training the at least one classifier for a first plurality of users associated with the organization, wherein the training comprises;

selecting the proper subset of the plurality of information items;

for each of the selected information items,

receiving, from each of the first plurality of users associated with the organization, feedback related to the selected information item,

for each of the plurality of tokens of the selected information item,

attributing the score to the token based at least in part on;

 

the received feedback from each of the first plurality of users associated with the organization, and

 

the reputation and influence of each of the first plurality of users associated with the organization.

8. The method of claim 1, wherein the received feedback is implicit feedback.

9. The method of claim 8, wherein the received implicit feedback includes at least one document view associated with an information item.

10. The method of claim 9, wherein the received implicit feedback includes at least one information item being forwarded to other users and at least one comment associated with the at least one information item.

11. The method of claim 8, wherein the received implicit feedback includes at least one form of negative feedback.

12. The method of claim 11, wherein the negative feedback includes deleting an information item.

13. The method of claim 1, wherein the received feedback is explicit feedback.

14. The method of claim 1, wherein a provided indication of a first information item of the first group of two or more information items within the organization that the specific user is likely to find interesting includes a first image selected from among a plurality of images associated with the first information item.

15. The method of claim 14, wherein selecting the first image comprises:

for each of the plurality of images associated with the first information item, determining a resolution of the image; and

selecting the image with the highest resolution from among the plurality of images associated with the first information item.

16. The method of claim 14, wherein selecting the first image comprises:

for each of the plurality of images associated with the first information item, determining a size of the image; and

selecting the image with the highest determined size from among the plurality of images associated with the first information item.

17. The method of claim 14, wherein selecting the first image comprises:

for each of the plurality of images associated with the first information item, determining a date and time of the image; and

selecting the image with the earliest determined date and time from among the plurality of images associated with the first information item.

18. A non-transitory computer-readable medium storing instructions that, if executed by a computing system having a processor, cause the computing system to perform a method for identifying information of interest within an organization, the method comprising:

determining use data that characterizes relationships among a plurality of information items;

for a plurality of users associated with the organization,

training at least one classifier based on the plurality of users associated with the organization, wherein the training comprises;

selecting a proper subset of the plurality of information items;

for each of the selected information items,

receiving, from a specific user associated with the organization, feedback indicating at least one preference of the specific user related to the selected information item, and

for each of a plurality of tokens associated with the selected information item,

 

attributing a score to the token based at least in part on the received feedback, and

 

storing the attributed score;

receiving an indication of a first group of two or more information items within the organization, wherein the first group of two or more information items within the organization includes at least two information items not included in the selected proper subset of the plurality of information items; and

for each of a plurality of information items of the first group of two or more information items within the organization,

for each of the specific user of the plurality of users associated with the organization,

accessing the at least one trained classifier that has been trained using feedback received from the specific user;

determining whether the specific user is likely to find the information item interesting based at least in part on the accessed at least one trained classifier, and

providing, for display, an indication of at least one information item of the first group of two or more information items within the organization that the specific user is likely to find interesting.

19. The non-transitory computer-readable medium of claim 18, the training further comprising:

for each of the selected information items,

for each of a plurality of features of the selected information item,

attributing a score to the feature based on feedback from at least one user.

20. The non-transitory computer-readable medium of claim 19, wherein determining whether the specific user is likely to find the information item interesting based at least in part on the at least one trained classifier comprises:

applying the at least one trained classifier to features of the information item.

21. A computing system, having one or more processors, comprising:

at least one of the one or more processors configured to determine use data that characterizes relationships among a plurality of information items;

at least one of the one or more processors configured to, for a plurality of users associated with the organization,

train at least one classifier based on the plurality of users associated with the organization, wherein the training comprises;

selecting a proper subset of the plurality of information items;

for each of the selected information items,

receiving, from a specific user associated with the organization, feedback indicating at least one preference of the specific user related to the selected information item, and

for each of a plurality of tokens associated with the selected information item,

 

attributing a score to the token based at least in part on the received feedback, and

 

storing the attributed score;

at least one of the one or more processors configured to receive an indication of a first group of two or more information items within the organization, wherein the first group of two or more information items within the organization include at least two information items not included in the selected proper subset of the plurality of information items; and

at least one of the one or more processors configured to, for each of a plurality of information items of the first group of two or more information items within the organization,

for each of the specific user of the plurality of users associated with the organization,

accessing the at least one trained classifier that has been trained using feedback received from the specific user,

determine whether the specific user is likely to find the information item interesting based at least in part on the at least one trained classifier, and

provide, for display, an indication of at least one information item of the first group of two or more information items within the organization that the specific user is likely to find interesting.

22. The computing system of claim 21, further comprising:

at least one of the one or more processors configured to periodically identify newly available information items; and

at least one of the one or more processors configured to, for each of the identified newly-available information items,

for each of the plurality of users,

applying the at least one trained classifier associated with the specific user to attributes of the newly-available information item to determine a relevance value,

determining that the determined relevance value exceeds a predetermined threshold, and

in response to determining that the determined relevance value exceeds the predetermined threshold, adding the newly-available information item to a collection of items associated with the specific user.

23. The computing system of claim 21, further comprising:

at least one of the one or more processors configured to, for a first information item,

determine a number of views of the first information item,

determine a duration of views of the first information item,

determine a number of likes of the first information item, and

determine a number of comments on the first information item.