Ranking test framework for search results on an online social network

1. A method comprising, by a computing device:

accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising:

a first-user node corresponding to a first user associated with an online social network; and

a plurality of second nodes that each correspond to a concept or a second user associated with the online social network;

receiving a first set of scored results from the first user comprising:

one or more results generated by a first search algorithm in response to a query from the first user, wherein the one or more results correspond to one or more second nodes, respectively, the one or more results being personalized for the first user based on social-graph information associated with the first user; and

one or more scores inputted by the first user corresponding to the one or more results, respectively;

calculating a discounted cumulative gain for each result in the first set of scored results based on the score inputted by the first user corresponding to the result; and

modifying the first search algorithm based on the calculated gain for each result, wherein the first search algorithm is modified to improve the ranking of search results personalized for the first user.

2. The method of claim 1 , wherein the first set of scored results comprises one or more tuples, each tuple comprising:

an identifier corresponding to the first-user node;

the query from the first user;

a result corresponding to one of the second nodes, wherein the result is generated by the first search algorithm in response to the query; and

a score corresponding to the result.

3. The method of claim 1 , wherein each result in the first set of scored results has a rank with respect to the other results in the first set of scored results.

4. The method of claim 3 , wherein calculating the gain for each result in the first set of scored results is further based on the rank of the result.

5. The method of claim 1 , further comprising:

transmitting a query template to the first user, wherein the query template comprises one or more fields where the first user can input a reference to a second node or an edge; and

receiving the query from the first user, wherein the query comprises references to one or more second nodes and one or more edges inputted by the first user.

6. The method of claim 1 , further comprising:

receiving the query from the first user; and

identifying one or more second nodes corresponding to the query; and

generating by the first search algorithm the one or more result, each result corresponding to one of the identified second nodes.

7. The method of claim 1 , wherein each query comprises references to one or more second nodes and one or more edges.

8. The method of claim 7 , wherein, for each result in the first set of scored results, the second node corresponding to the result is connected to at least one of the second nodes referenced in the query by at least one of the edges referenced in the query.

9. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:

access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising:

a first-user node corresponding to a first user associated with an online social network; and

a plurality of second nodes that each correspond to a concept or a second user associated with the online social network;

receive a first set of scored results from the first user comprising:

one or more results generated by a first search algorithm in response to a query from the first user, wherein the one or more results correspond to one or more second nodes, respectively, the one or more results being personalized for the first user based on social-graph information associated with the first user; and

one or more scores inputted by the first user corresponding to the one or more results, respectively;

calculate a discounted cumulative gain for each result in the first set of scored results based on the score inputted by the first user corresponding to the result; and

modify the first search algorithm based on the calculated gain for each result, wherein the first search algorithm is modified to improve the ranking of search results personalized for the first user.

10. The system of claim 9 , wherein the first set of scores results comprises one or more tuples, each tuple comprising:

an identifier corresponding to the first-user node;

the query from the first user;

a result corresponding to one of the second nodes, wherein the result is generated by the first search algorithm in response to the query; and

a score corresponding to the result.

11. The system of claim 9 , wherein each result in the first set of scored results has a rank with respect to the other results in the first set of scored results.

12. The system of claim 11 , wherein calculating the gain for each result in the first set of scored results is further based on the rank of the result.

13. The system of claim 9 , wherein the processors are further operable when executing the instructions to:

transmit a query template to the first user, wherein the query template comprises one or more fields where the first user can input a reference to a second node or an edge; and

receive the query from the first user, wherein the query comprises references to one or more second nodes and one or more edges inputted by the first user.

14. The system of claim 9 , wherein the processors are further operable when executing the instructions to:

receive the query from the first user; and

identify one or more second nodes corresponding to the query; and

generate by the first search algorithm the one or more result, each result corresponding to one of the identified second nodes.

15. The system of claim 9 , wherein each query comprises references to one or more second nodes and one or more edges.

16. The system of claim 15 , wherein, for each result in the first set of scored results, the second node corresponding to the result is connected to at least one of the second nodes referenced in the query by at least one of the edges referenced in the query.

17. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising:

a first-user node corresponding to a first user associated with an online social network; and

a plurality of second nodes that each correspond to a concept or a second user associated with the online social network;

receive a first set of scored results from the first user comprising:

one or more results generated by a first search algorithm in response to a query from the first user, wherein the one or more results correspond to one or more second nodes, respectively, the one or more results being personalized for the first user based on social-graph information associated with the first user; and

one or more scores inputted by the first user corresponding to the one or more results, respectively;

calculate a discounted cumulative gain for each result in the first set of scored results based on the score inputted by the first user corresponding to the result; and

modify the first search algorithm based on the calculated gain for each result, wherein the first search algorithm is modified to improve the ranking of search results personalized for the first user.

18. The method of claim 1 , wherein calculating the discounted cumulative gain (DCG) for each result in the first set of scored results, wherein:

DCG

p

=

s

1

+

∑

i

=

2

p

⁢

⁢

s

i

log

2

⁡

(

i

)

,

and wherein

DCG p =the discounted cumulative gain of a result having rank p, and

s i is the score corresponding to the result r i .