Smart software recommendation using an application network
Recommending applications is disclosed. An application graph is disclosed that represents applications. Each node of the graph corresponds to an application and edges relate applications that can handle the same file type. When an input application is identified, the graph can be used to recommend other applications that may be a suitable replacement for the input application. The recommendation is based on the graph and its links and on characteristics of the organization.
1. A method, comprising:
inputting an application into a graph associated with a recommendation engine, wherein the input application is associated with a first node and wherein the graph is associated with nodes, the nodes including the first node and second nodes;
determining a link score for the second nodes in the graph linked to the first node;
determining an organization score for an organization for each of the second nodes in the graph, wherein the organization score is based on whether applications corresponding to the second nodes are already deployed in a computing system for the organization or whether licenses for applications corresponding to the second nodes have been acquired by the organization, wherein the link score is combined with the organization score to determine an overall score for each of the second nodes, wherein the organization score is determined by accounting for license availability, use in an organization, and compatibility with operating system; and
recommending at least one of the second nodes for use on the computing system, wherein each of the second nodes corresponds to a different application.
2. The method of claim 1 , further wherein the graph comprises edges that connect the nodes, wherein each of the edges includes a weight and wherein edges represent whether the corresponding nodes share at least one characteristic.
3. The method of claim 2 , wherein the at least one characteristic includes file type.
4. The method of claim 2 , wherein the graph comprises communities of nodes, wherein the communities of nodes identify applications that share a purpose.
5. The method of claim 1 , wherein the link score accounts for edge weights, community distance, and user reviews.
6. The method of claim 5 , wherein the link score includes Link Scoreij=f(Kij)−g(CDistij)+h(Uij).
7. The method of claim 1 , further comprising prompting a user for a review of the recommendation.
8. The method of claim 1 , further comprising performing link prediction to generate new edges between nodes in the graph.
9. The method of claim 1 , further comprising recommending at least one of the second nodes without user interaction.
10. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
inputting an application into a graph associated with a recommendation engine, wherein the input application is associated with a first node and wherein the graph is associated with nodes, the nodes including the first node and second nodes;
determining a link score for the second nodes in the graph linked to the first node;
determining an organization score for an organization for each of the second nodes in the graph, wherein the organization score is based on whether applications corresponding to the second nodes are already deployed in a computing system for the organization or whether licenses for applications corresponding to the second nodes have been acquired by the organization, wherein the link score is combined with the organization score to determine an overall score for each of the second nodes, wherein the organization score is determined by accounting for license availability, use in an organization, and compatibility with operating system; and
recommending at least one of the second nodes for use on the computing system, wherein each of the second nodes corresponds to a different application.
11. The non-transitory storage medium of claim 10 , wherein the graph comprises edges that connect the nodes, wherein each of the edges includes a weight and wherein edges represent whether the corresponding nodes share at least one characteristic.
12. The non-transitory storage medium of claim 11 , wherein the at least one characteristic includes file type.
13. The non-transitory storage medium of claim 11 , wherein the graph comprises communities of nodes, wherein the communities of nodes identify applications that share a purpose.
14. The non-transitory storage medium of claim 10 , wherein the link score accounts for edge weights, community distance, and user reviews.
15. The non-transitory storage medium of claim 14 , wherein the link score includes Link Scoreij=f(Kij)−g(CDistij)+h(Uij).
16. The non-transitory storage medium of claim 10 , further comprising prompting a user for a review of the recommendation.
17. The non-transitory storage medium of claim 10 , further comprising performing link prediction to generate new edges between nodes in the graph.
18. The non-transitory storage medium of claim 10 , further comprising recommending at least one of the second nodes without user interaction.