CONTEXT AWARE DYNAMIC CANDIDATE POOL RETRIEVAL AND RANKING
Disclosed in some examples are methods, systems, and machine readable mediums which provide for retrieval, ranking, and display of candidates that are more likely to respond to employment inquiries in an employment search graphical user interface (GUI). The system may employ a machine learning algorithm which may calculate a score for each member of the social networking service that predicts, based upon one or more features how likely the individual is to respond to a message. In some examples, the candidates that are determined to be more likely to respond may be presented as a selectable option in the GUI.
1 . A method for providing a graphical user interface (GUI), the method comprising:
using one or more processors of a social networking service, executing operations of:
for each particular member of a plurality of members of the social networking service, determining, using a machine learning algorithm, a response score for the particular member based upon one or more of: activity data describing the particular member's activity on the social networking service, and profile data, the response score indicative of a likelihood of the particular member to respond to an inquiry about a job from another member of the social networking service;
causing a search GUI to be displayed to a user, the search GUI including GUI elements that receive search criteria;
receiving through the search GUI, a search query with member search criteria;
determining a search context of the user;
determining a threshold based upon the search context;
responsive to the search query, identifying a set of members from the plurality of members based on the response score determined for each particular member from the set of members and the threshold; and
causing the set of members to be displayed to the user as search results for the search query.
2 . The method of claim 1 , wherein the search context comprises the search criteria.
3 . The method of claim 1 , wherein determining the threshold comprises utilizing at least one if-then rule.
4 . The method of claim 1 , wherein determining the threshold comprises inputting the search context to a machine learning model.
5 . The method of claim 4 , comprising:
training a machine learning model with the machine learning algorithm and labelled training data that comprises historical search contexts from a plurality of previous searches.
6 . The method of claim 5 , comprising:
automatically determining a label for the training data based upon a number of returned search results.
7 . The method of claim 6 , wherein the label indicates to increase the threshold when the number of returned search results is below a predetermined number of search results.
8 . A non-transitory machine readable medium comprising instructions, that when executed by a machine, cause the machine to perform operations comprising:
for each particular member of a plurality of members of the social networking service, determining, using a machine learning algorithm, a response score for the particular member based upon one or more of: activity data describing the particular member's activity on the social networking service, and profile data, the response score indicative of a likelihood of the particular member to respond to an inquiry about a job from another member of the social networking service;
causing a search GUI to be displayed to a user, the search GUI including GUI elements that receive search criteria;
receiving through the search GUI, a search query with member search criteria;
determining a search context of the user;
determining a threshold based upon the search context;
responsive to the search query, identifying a set of members from the plurality of members based on the response score determined for each particular member from the set of members and the threshold; and
causing the set of members to be displayed to the user as search results for the search query.
9 . The machine-readable medium of claim 8 , wherein the search context comprises the search criteria.
10 . The machine-readable medium of claim 8 , wherein the operations of determining the threshold comprises utilizing at least one if-then rule.
11 . The machine-readable medium of claim 8 , wherein the operations of determining the threshold comprises inputting the search context to a machine learning model.
12 . The machine-readable medium of claim 11 , wherein the operations further comprise:
training a machine learning model with the machine learning algorithm and labelled training data that comprises historical search contexts from a plurality of previous searches.
13 . The machine-readable medium of claim 12 , wherein the operations further comprise:
automatically determining a label for the training data based upon a number of returned search results.
14 . The machine-readable medium of claim 13 , wherein the label indicates to increase the threshold when the number of returned search results is below a predetermined number of search results.
15 . A system for providing a Graphical User Interface (GUI), the system comprising:
a processor;
a memory communicatively coupled to the processor and comprising instructions, which when executed by the processor, causes the processor to perform operations comprising:
for each particular member of a plurality of members of the social networking service, determining, using a machine learning algorithm, a response score for the particular member based upon one or more of activity data describing the particular member's activity on the social networking service, and profile data, the response score indicative of a likelihood of the particular member to respond to an inquiry about a job from another member of the social networking service;
causing a search GUI to be displayed to a user, the search GUI including GUI elements that receive search criteria;
receiving through the search GUI, a search query with member search criteria;
determining a search context of the user;
determining a threshold based upon the search context;
responsive to the search query, identifying a set of members from the plurality of members based on the response score determined for each particular member from the set of members and the threshold; and
causing the set of members to be displayed to the user as search results for the search query.
16 . The system of claim 15 , wherein the search context comprises the search criteria.
17 . The system of claim 15 , wherein the operations of determining the threshold comprises utilizing at least one if-then rule.
18 . The system of claim 15 , wherein the operations of determining the threshold comprises inputting the search context to a machine learning model.
19 . The system of claim 18 , wherein the operations further comprise:
training a machine learning model with the machine learning algorithm and labelled training data that comprises historical search contexts from a plurality of previous searches.
20 . The system of claim 19 , wherein the operations further comprise:
automatically determining a label for the training data based upon a number of returned search results.
21 . The system of claim 20 , wherein the label indicates to increase the threshold when the number of returned search results is below a predetermined number of search results.