An application recommending method includes acquiring a first behavior eigenvalue according to behavior data of a first user. It is determined that at least one second user from a user relationship chain of the first user according to the first behavior eigenvalue. The second user is on the user relationship chain of the first user. A degree of similarity between a second behavior eigenvalue and the first behavior eigenvalue is greater than a preset threshold, and the second behavior eigenvalue is a behavior eigenvalue of the second user. A to-be-recommended application for the first user on the basis of behavior data of the at least one second user is determined.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An application recommending method, comprising: acquiring a first behavior eigenvalue according to behavior data of a first user; determining at least one second user from a user relationship chain of the first user according to the first behavior eigenvalue, the second user being on the user relationship chain of the first user, a degree of similarity between a second behavior eigenvalue and the first behavior eigenvalue being greater than a preset threshold, and the second behavior eigenvalue being a behavior eigenvalue of the second user; and determining a to-be-recommended application for the first user on the basis of behavior data of the at least one second user. 2. The method according to claim 1, wherein the acquiring a first behavior eigenvalue according to behavior data of a first user comprises: acquiring a real-time behavior eigenvalue according to real-time behavior data of the first user; acquiring a historical behavior eigenvalue according to historical behavior data of the first user; and acquiring the first behavior eigenvalue of the first user according to the real-time behavior eigenvalue and the historical behavior eigenvalue. 3. The method according to claim 1, wherein the method further comprises: determining, on the basis of a current user relationship chain and a historical user relationship chain of the first user, a user deleted from the historical user relationship chain with respect to the current user relationship chain, and determining a deleted user as the second user if a degree of similarity between a behavior eigenvalue of the deleted user and the first behavior eigenvalue is greater than the preset threshold. 4. The method according to claim 1, wherein the acquiring a first behavior eigenvalue according to behavior data of a first user comprises: acquiring an eigenvalue of each piece of behavior data of the first user; and computing the first behavior eigenvalue according to the eigenvalue of each piece of behavior data and a preset weight value of each piece of behavior data. 5. The method according to claim 1, wherein before the acquiring a first behavior eigenvalue according to behavior data of a first user, the method further comprises: acquiring a scheduling weight of each compute node; and scheduling, according to the scheduling weight of each compute node, at least one compute node of multiple compute nodes to execute the procedure of acquiring a first behavior eigenvalue according to behavior data of a first user. 6. The method according to claim 5, wherein the acquiring a scheduling weight of each compute node comprises: acquiring, for one compute node of the multiple compute nodes, a computation delay of the compute node and a resource utilization of the compute node; computing a system load of the compute node according to the computation delay and the resource utilization; and acquiring the scheduling weight of the compute node according to the system load of the compute node and system loads of other compute nodes. 7. The method according to claim 6, wherein the following formula computes the system load of the compute node according to the computation delay and the resource utilization uses: Zi=Ri*0.8*α+0.2/Ti*β wherein Z1 denotes a system load of the ith compute node, Ri denotes a resource utilization of the ith compute node, Ti denotes a computation delay of the ith compute node, and α and β are both constants. 8. The method according to claim 7, wherein the following formula acquires the scheduling weight of the compute node according to the system load of the compute node and system loads of other compute nodes: wi=(1/Zi)/(1/Zi+1/Z2+ . . . +1/Zn) wherein wi denotes a scheduling weight of the ith compute node, Z1 denotes a system load of the first compute node, Z2 denotes a system load of the second compute node, and Zn denotes a system load of the nth compute node. 9. The method according to claim 4, further comprising: scheduling, when it is detected that an exception occurs in a compute node currently executing an application recommending task, a backup compute node of the compute node to execute the application recommending task. 10. An application recommending apparatus, comprising: a behavior eigenvalue acquiring module, configured to acquire a first behavior eigenvalue according to behavior data of a first user; a second user determining module, configured to determine at least one second user from a user relationship chain of the first user according to the first behavior eigenvalue, the second user being on the user relationship chain of the first user, a degree of similarity between a second behavior eigenvalue and the first behavior eigenvalue being greater than a preset threshold, and the second behavior eigenvalue being a behavior eigenvalue of the second user; and an application determining module, configured to determine a to-be-recommended application for the first user on the basis of behavior data of the at least one second user. 11. The apparatus according to claim 10, wherein the behavior eigenvalue acquiring module is configured to acquire a real-time behavior eigenvalue according to real-time behavior data of the first user; acquire a historical behavior eigenvalue according to historical behavior data of the first user; and acquire the first behavior eigenvalue of the first user according to the real-time behavior eigenvalue and the historical behavior eigenvalue. 12. The apparatus according to claim 10, wherein the second user determining module is further configured to determine, on the basis of a current user relationship chain and a historical user relationship chain of the first user, a user deleted from the historical user relationship chain with respect to the current user relationship chain, and determine a deleted user as the second user if a degree of similarity between a behavior eigenvalue of the deleted user and the first behavior eigenvalue is greater than the preset threshold. 13. The apparatus according to claim 10, wherein the behavior eigenvalue acquiring module is configured to acquire an eigenvalue of each piece of behavior data of the first user; and compute the first behavior eigenvalue according to the eigenvalue of each piece of behavior data and a preset weight value of each piece of behavior data. 14. The apparatus according to claim 10, further comprising: a scheduling weight acquiring module, configured to acquire a scheduling weight of each compute node; and a scheduling module, configured to schedule, according to the scheduling weight of each compute node, at least one compute node of multiple compute nodes to execute the procedure of acquiring a first behavior eigenvalue according to behavior data of a first user. 15. The apparatus according to claim 14, wherein the scheduling weight acquiring module is configured to acquire, for one compute node of the multiple compute nodes, a computation delay of the compute node and a resource utilization of the compute node; compute a system load of the compute node according to the computation delay and the resource utilization; and acquire the scheduling weight of the compute node according to the system load of the compute node and system loads of other compute nodes. 16. The apparatus according to claim 15, wherein the scheduling weight acquiring module uses the following formula to compute the system load of the compute node: Zi=Ri*0.8*α+0.2/Ti*β wherein Z1 denotes a system load of the ith compute node, Ri denotes a resource utilization of the ith compute node, Ti denotes a computation delay of the ith compute node, and α and β are both constants. 17. The apparatus according to claim 16, wherein the scheduling weight acquiring module uses the following formula to compute the scheduling weight of the compute node: wi=(1/Zi)/(1/Zi+1/Z2+ . . . +1/Zn) wherein wi denotes a scheduling weight of the ith compute node, Z1 denotes a system load of the first compute node, Z2 denotes a system load of the second compute node, and Zn denotes a system load of the nth compute node. 18. The apparatus according to claim 13, wherein the scheduling module is further configured to schedule, when it is detected that an exception occurs in a compute node currently executing an application recommending task, a backup compute node of the compute node to execute the application recommending task.