Connection recommendation and collaboration
Example methods and systems for connection recommendation are provided. A communication platform provides a user hub application comprising one or more application modules corresponding to one or more user applications. The communication platform accesses user data associated with a plurality of users at the one or more user applications via the user hub application. The plurality of users includes a first user and a set of other users. The communication platform determines a connection recommendation for the first user based on the user data and provides the connection recommendation to the first user via the user hub application.
1 . A method comprising:
providing, by a communication platform, a user hub application comprising one or more application modules corresponding to one or more user applications;
accessing, by the communication platform, user data associated with a plurality of users from the one or more user applications via the user hub application, wherein the plurality of users comprises a first user and a set of other users;
determining a connection recommendation for the first user based on the user data, comprising:
determining a first embedding vector for the first user based on a first set of user data corresponding to the first user;
determining a second embedding vector for a second user based on a second set of user data corresponding to the second user;
determining a similarity score for the second user based on first embedding vector for the first user and the second embedding vector for the second user; and
in response to determining that the similarity score is above a predetermined threshold, selecting the second user as the connection recommendation; and
providing the connection recommendation to the first user via the user hub application.
2 . The method of claim 1 , wherein providing a user hub application comprising one or more application modules comprises using a micro-frontend architecture to integrate the one or more applications modules with the user hub application.
3 . The method of claim 1 , wherein the one or more user applications comprise at least one third-party application.
4 . The method of claim 1 , wherein the user data comprises user metadata and user activity data, wherein the user metadata comprises name, location, education, job title, department, hobby, contact information, user connections, and joined channels or groups, wherein the user activity data comprises virtual meeting data, chat data, search data, email data, and calendar data.
5 . The method of claim 1 , further comprising enabling the first user to search for other users using keyword description, wherein the keyword description comprises name, location, job title, department, or hobby.
6 . The method of claim 1 , wherein determining one or more connection recommendations for the first user based on the user data comprises:
identifying a user similar to the first user using a collaborative filtering algorithm.
7 . The method of claim 1 , further comprising:
determining one or more top collaborators for the first user based on the user data; and
providing an indication of the one or more top collaborators to the first user.
8 . A system comprising:
a communications interface;
a non-transitory computer-readable medium; and
one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to:
provide a user hub application comprising one or more application modules corresponding to one or more user applications;
access user data associated with a plurality of users from the one or more user applications via the user hub application, wherein the plurality of users comprises a first user and a set of other users;
determine a connection recommendation for the first user based on the user data, comprising:
determining a first embedding vector for the first user based on a first set of user data corresponding to the first user;
determining a second embedding vector for a second user based on a second set of user data corresponding to the second user;
determining a similarity score for the second user based on first embedding vector for the first user and the second embedding vector for the second user; and
in response to determining that the similarity score is above a predetermined threshold, selecting the second user as the connection recommendation; and
provide the connection recommendation to the first user via the user hub application.
9 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:
using a micro-frontend architecture to integrate the one or more application modules into the user hub application.
10 . The system of claim 8 , wherein the user data comprises user metadata and user activity data, wherein the user metadata comprises name, location, education, job title, department, hobby, contact information, user connections, and joined channels or groups, wherein the user activity data comprises virtual meeting data, chat data, search data, email data, and calendar data.
11 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:
enable the first user to search for other users using keyword description, wherein the keyword description comprises name, location, job title, department, or hobby.
12 . The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:
determine one or more connection recommendations for the first user based on the user data using a collaborative filtering algorithm.
13 . A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to:
provide a user hub application comprising one or more application modules corresponding to one or more user applications;
access user data associated with a plurality of users from the one or more user applications via the user hub application, wherein the plurality of users comprises a first user and a set of other users;
determine a connection recommendation for the first user based on the user data, comprising:
determining a first embedding vector for the first user based on a first set of user data corresponding to the first user;
determining a second embedding vector for a second user based on a second set of user data corresponding to the second user;
determining a similarity score for the second user based on first embedding vector for the first user and the second embedding vector for the second user; and
in response to determining that the similarity score is above a predetermined threshold, selecting the second user as the connection recommendation; and
provide the connection recommendation to the first user via the user hub application.
14 . The non-transitory computer-readable medium of claim 13 , further comprising processor-executable instructions configured to cause one or more processors to:
using a micro-frontend architecture to integrate the one or more application modules into the user hub application.
15 . The non-transitory computer-readable medium of claim 14 , further comprising processor-executable instructions configured to cause one or more processors to:
enable the first user to search for other users using keyword description, wherein the keyword description comprises name, location, job title, department, or hobby.
16 . The non-transitory computer-readable medium of claim 13 , further comprising processor-executable instructions configured to cause one or more processors to:
determine one or more connection recommendations for the first user based on the user data using a collaborative filtering algorithm.
17 . The non-transitory computer-readable medium of claim 13 , further comprising processor-executable instructions configured to cause one or more processors to:
determine one or more top collaborators for the first user based on the user data; and
provide an indication of the one or more top collaborators to the first user.