Knowledge automation techniques may include generating, for each user of a plurality of users, a user vector associated with the user, and grouping the generated user vectors into clusters based on a clustering distance metric between the user vectors. For each cluster, the techniques may include determining a centroid of the cluster, and associating with the cluster at least some of the knowledge elements that the users associated with the cluster has interacted with. The techniques may further include comparing a target user vector of a target user with the centroids of the clusters to determine a matching cluster for the target user, and providing one or more recommendations of the knowledge elements that are associated with the matching cluster to the target user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: for each user of a plurality of users of a data processing system, generating, by the data processing system, a user vector associated with the user, wherein the user vector includes one or more of seeded profile information of the user, interaction data of user interactions with knowledge elements of the data processing system, and knowledge element metadata of the knowledge elements that the user interacted with; grouping, by the data processing system, the generated user vectors into clusters based on a clustering distance metric between the user vectors; for each cluster: determining a centroid of the cluster; and associating with the cluster at least some of the knowledge elements that the users associated with the cluster has interacted with; comparing, by the data processing system, a target user vector of a target user with the centroids of the clusters to determine a matching cluster for the target user; and providing, by the data processing system, one or more recommendations of the knowledge elements that are associated with the matching cluster to the target user. 2. The method of claim 1, wherein the seeded profile information, the interaction data, and the knowledge element metadata are maskable when grouping the generated user vectors into the clusters. 3. The method of claim 1, wherein the seeded profile information of the user includes one or more of a job junction of the user, a role of the user, an expertise of the user, an age of the user, a location of the user, and a gender of the user. 4. The method of claim 1, wherein the user interactions between the user and a knowledge element includes one or more of viewing the knowledge element, commenting on the knowledge element, rating of the knowledge element, sharing of the knowledge element, and publishing of the knowledge element performed by the user. 5. The method of claim 1, wherein the knowledge element metadata of a knowledge element includes one or more of key terms associated with the knowledge element, a publisher of the knowledge element, a title of the knowledge element, a topic of the knowledge element, a category associated with the knowledge element, and a timestamp of the knowledge element. 6. The method of claim 1, wherein providing the one or more recommendations includes filtering out knowledge elements that the target user has consumed. 7. The method of claim 1, wherein providing the one or more recommendations includes filtering out knowledge elements that are stale. 8. A non-transitory computer-readable storage memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising: instructions that cause the one or more processors to, for each user of a plurality of users, generate a user vector associated with the user, wherein the user vector includes one or more of seeded profile information of the user, interaction data of user interactions with knowledge elements of the data processing system, and knowledge element metadata of the knowledge elements that the user interacted with; instructions that cause the one or more processors to group the generated user vectors into clusters based on a clustering distance metric between the user vectors; instructions that cause the one or more processors to, for each cluster, determine a centroid of the cluster, and associate with the cluster at least some of the knowledge elements that the users associated with the cluster has interacted with; instructions that cause the one or more processors to compare a target user vector of a target user with the centroids of the clusters to determine a matching cluster for the target user; and instructions that cause the one or more processors to provide one or more recommendations of the knowledge elements that are associated with the matching cluster to the target user. 9. The non-transitory computer-readable storage memory of claim 8, wherein the seeded profile information, the interaction data, and the knowledge element metadata are maskable when grouping the generated user vectors into the clusters. 10. The non-transitory computer-readable storage memory of claim 8, wherein the seeded profile information of the user includes one or more of a job junction of the user, a role of the user, an expertise of the user, an age of the user, a location of the user, and a gender of the user. 11. The non-transitory computer-readable storage memory of claim 8, wherein the user interactions between the user and a knowledge element includes one or more of viewing the knowledge element, commenting on the knowledge element, rating of the knowledge element, sharing of the knowledge element, and publishing of the knowledge element performed by the user. 12. The non-transitory computer-readable storage memory of claim 8, wherein the knowledge element metadata of a knowledge element includes one or more of key terms associated with the knowledge element, a publisher of the knowledge element, a title of the knowledge element, a topic of the knowledge element, a category associated with the knowledge element, and a timestamp of the knowledge element. 13. The non-transitory computer-readable storage memory of claim 8, wherein the plurality of instructions further includes instructions that cause the one or more processors to filter out knowledge elements that the target user has consumed when providing the one or more recommendations. 14. The non-transitory computer-readable storage memory of claim 8, wherein the plurality of instructions further includes instructions that cause the one or more processors to filter out knowledge elements that are stale when providing the one or more recommendations. 15. A system comprising: one or more processors; and a memory coupled with and readable by the one or more processors, the memory configured to store a set of instructions which, when executed by the one or more processors, causes the one or more processors to: for each user of a plurality of users, generate a user vector associated with the user, wherein the user vector includes one or more of seeded profile information of the user, interaction data of user interactions with knowledge elements of the data processing system, and knowledge element metadata of the knowledge elements that the user interacted with; group the generated user vectors into clusters based on a clustering distance metric between the user vectors; for each cluster: determine a centroid of the cluster; and associate with the cluster at least some of the knowledge elements that the users associated with the cluster has interacted with; compare a target user vector of a target user with the centroids of the clusters to determine a matching cluster for the target user; and provide one or more recommendations of the knowledge elements that are associated with the matching cluster to the target user. 16. The system of claim 15, wherein the seeded profile information, the interaction data, and the knowledge element metadata are maskable when grouping the generated user vectors into the clusters. 17. The system of claim 15, wherein the seeded profile information of the user includes one or more of a job junction of the user, a role of the user, an expertise of the user, an age of the user, a location of the user, and a gender of the user. 18. The system of claim 15, wherein the user interactions between the user and a knowledge element includes one or more of viewing the knowledge element, commenting on the knowledge element, rating of the knowledge element, sharing of the knowledge element, and publishing of the knowledge element performed by the user. 19. The system of claim 15, wherein the knowledge element metadata of a knowledge element includes one or more of key terms associated with the knowledge element, a publisher of the knowledge element, a title of the knowledge element, a topic of the knowledge element, a category associated with the knowledge element, and a timestamp of the knowledge element. 20. The system of claim 19, wherein the set of instructions further comprises instructions, which when executed by the one or more processors, causes the one or more processors to filter out knowledge elements that the target user has consumed when providing the one or more recommendations.