Issues recommendations using machine learning

1. A computer-implemented method comprising:

receiving a query requesting issues relevant to a user;

retrieving a plurality of issues responsive to the query;

generating a user similarity matrix by generating a plurality of user profile vectors and calculating a similarity between each pair of user profile vectors;

first ranking the retrieved issues using a first machine learning model to result in a first subset of the retrieved issues;

second ranking the first subset of the retrieved issues using a second, different machine learning model to result in a second subset of the retrieved issues, the second subset of the retrieved issues being a subset of the first subset of the retrieved issues; and

providing data responsive to the query comprising at least a portion of the second subset of the retrieved issues;

wherein generating a user profile vector comprises:

scoring each issue based on user actions stored in a user actions log;

ranking keywords for the user based on the scoring of the issues to generate a list of top keywords; and

generating user keywords vectors based on the list of top keywords.

2. The method of claim 1 , wherein the first machine learning model comprises a logistic regression model.

3. The method of claim 2 , wherein the second machine learning model comprises a neural network model.

4. The method of claim 3 , wherein the neural network model comprises a DeepFM model.

5. The method of claim 1 , wherein the plurality of issues are retrieved using different retrieval modalities.

6. The method of claim 5 , wherein one of the retrieval modalities comprises:

predicting scores for the user against each issue using an alternating least squares algorithm;

ranking the issues according to their scores; and

filtering out scores below a pre-defined threshold.

7. The method of claim 6 further comprising:

filtering the retrieved plurality of issues based on collaborative filtering in relation to issues.

8. The method of claim 7 , wherein the collaborative filtering in relation to issues uses an issues similarity matrix.

9. The method of claim 8 further comprising:

generating the issues similarity matrix by:

identifying, based on issues data, a predefined number of top keywords associated with the user using a term frequency-inverse document frequency; and

generating, based on issues data, a plurality of word vectors using a word embedding model;

generating a plurality of issue vectors based on the identification of the predefined number of top keywords and the generated plurality of word vectors;

calculating a similarity between each pair of issue vectors to result in the issues similarity matrix.

10. The method of claim 6 further comprising:

filtering the retrieved plurality of issues based on collaborative filtering in relation to users.

11. The method of claim 10 , wherein the collaborative filtering in relation to users uses the user similarity matrix.

12. The method of claim 1 , wherein each user profile vector is generated by concatenating a user keywords vector with a user basic vector.

13. The method of claim 12 , wherein the user keywords vector is derived from the user actions log which comprises information comprising a user identification (ID), issue ID, a type of action associated with the issue, a timestamp for the action, and a duration of the action.

14. The method of claim 1 , wherein the providing data comprises at least one of: causing the data to be displayed in a graphical user interface, loading the data into memory, storing the data in physical persistence, or transmitting the data to a remote computing system.

15. A system comprising:

at least one data processor; and

memory storing instructions which, when executed by the at least one data processor, result in operations comprising:

receiving a query requesting issues relevant to a user;

retrieving a plurality of issues responsive to the query;

generating a user similarity matrix by generating a plurality of user profile vectors and calculating a similarity between each pair of user profile vectors;

first ranking the retrieved issues using a first machine learning model to result in a first subset of the retrieved issues;

second ranking the first subset of the retrieved issues using a second, different machine learning model to result in a second subset of the retrieved issues, the second subset of the retrieved issues being a subset of the first subset of the retrieved issues; and

providing data responsive to the query comprising at least a portion of the second subset of the retrieved issues,

wherein generating a user profile vector comprises:

scoring each issue based on user actions stored in a user actions log;

ranking keywords for the user based on the scoring of the issues to generate a list of top keywords; and

generating user keywords vectors based on the list of top keywords.

16. The system of claim 15 further comprising:

an in-memory database which receives the query and retrieves the plurality of issues responsive to the query.

17. A non-transitory computer program product storing instructions which, when executed by at least one computing device, results in operations comprising:

receiving a query requesting issues relevant to a user;

retrieving a plurality of issues responsive to the query;

generating a user similarity matrix by generating a plurality of user profile vectors and calculating a similarity between each pair of user profile vectors;

first ranking the retrieved issues using a first machine learning model to result in a first subset of the retrieved issues;

second ranking the first subset of the retrieved issues using a second, different machine learning model to result in a second subset of the retrieved issues, the second subset of the retrieved issues being a subset of the first subset of the retrieved issues; and

providing data responsive to the query comprising at least a portion of the second subset of the retrieved issues,

wherein generating a user profile vector comprises:

scoring each issue based on user actions stored in a user actions log;

ranking keywords for the user based on the scoring of the issues to generate a list of top keywords; and

generating user keywords vectors based on the list of top keywords.

18. The non-transitory computer program product of claim 17 , wherein the first machine learning model comprises a logistic regression model and the second machine learning model comprises a DeepFM model.