Conditioned Search Ranking Models on Online Social Networks

• US 20160041982A1
• Filed: 08/05/2014
• Published: 02/11/2016
• Est. Priority Date: 08/05/2014
• Status: Abandoned Application

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Raina, Rajat, Boucher, Maxime, Li, Xiao, He, Junfeng, Scheau, Cristina
• Application Number:
  US14/452,307
• Publication Number:
  US 20160041982A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

In one embodiment, a method includes a computing system receiving a query from a first user, which can be parsed to identify i conditions associated with the query. The system may then identify one or more search results substantially matching the i conditions. Each search result may be associated with a feature vector of j features. The system may then access a conditioned ranking model that comprises j scoring functions for each i condition. The j scoring functions may correspond to j features of the feature vectors. A score for each search result may be calculated based on the i conditions and the j features. The system may then receive a selection of one of the search results from the first user, and in response modify one or more of the j scoring functions of the conditioned ranking model based on the selection.

Claims

1. A method comprising, by one or more computing devices:

receiving a query from a first user;

parsing the received query to identify i conditions associated with the received query;

identifying one or more search results substantially matching the i conditions associated with the received query, each search result being associated with a feature vector of j features;

accessing a conditioned ranking model, wherein the conditioned ranking model comprises;

for each i condition, j scoring functions corresponding to j features of the feature vector associated with each search result; and

a ranking algorithm for determining a rank of each search result;

calculating a score for each search result based at least in part on the i conditions and the j features as associated with each i condition;

receiving, from the first user, a selection of one of the search results; and

modifying one or more of the j scoring functions of the conditioned ranking model based at least in part on the selected search result by the first user.

2. The method of claim 1, wherein i and j are non-zero positive integers.

3. The method of claim 1, wherein the i conditions comprise one or more query constraints associated with the received query.

4. The method of claim 1, wherein the conditioned ranking model is implemented by a tree data structure.

5. The method of claim 4, wherein the tree data structure comprises:

the i conditions forming one or more levels of condition parent nodes; and

each feature vector of j features forming j terminal nodes, wherein;

the j terminal nodes are sibling nodes to an associated condition parent node;

each j terminal node stores a feature score for an associated feature, the feature score being determined at least by a scoring function of the associated feature.

6. The method of claim 5, wherein each condition parent node stores a condition score for an associated condition of the received query, the condition score being determined at least by the feature scores of its associated j sibling terminal nodes.

7. The method of claim 5, where a root node of the conditioned ranking model stores a score of a search result associated with the received query.

8. The method of claim 1, wherein at least one of the scoring function is a piecewise function.

9. The method of claim 8, wherein the piecewise function of a scoring function is associated with a continuous feature.

10. The method of claim 1, wherein at least one of the scoring functions is a step function associated with a discrete feature.

11. The method of claim 1, wherein each scoring function is determined at least in part by the associated condition of the received query and the first user.

12. The method of claim 1, wherein the score is calculated using the ranking algorithm, and wherein the ranking algorithm is a linear ranking algorithm.

13. The method of claim 1, wherein the score for a search result is calculated at least by Σ

_n=1ⁱ Σ

_m=1^j score_n(m) where score_n(m) corresponds to an output of a scoring function associated with a m^th feature and a n^th condition.

14. The method of claim 1, wherein the ranking algorithm determines a rank for a search result based at least in part on the calculated score for the search result.

15. The method of claim 1, prior to receiving the selection of one of the search results from the first user, further comprising:

ranking the scored search results; and

presenting, to the first user, at least a pre-determined portion of the search results as ranked.

16. The method of claim 15, further comprising:

comparing the ranks of the scored search results with the selected search result by the first user; and

adjusting the ranking algorithm based at least in part on a result of the comparison, wherein the adjustment is performed pointwise, pairwise, or listwise.

17. The method of claim 1, wherein receiving the selection of one of the search results from the first user comprises determining an interaction associated with the selected search result, wherein the interaction is a click, a like, or a comment.

18. The method of claim 1, wherein modifying the conditioned ranking model comprises:

comparing outputs of the scoring functions with the selected search results by the first user; and

adjusting one or more of the j scoring functions based at least in part on a result of the comparison, wherein the adjustment is performed manually or by a machine learning algorithm.

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

receive a query from a first user;

parse the received query to identify i conditions associated with the received query;

identify one or more search results substantially matching the i conditions associated with the received query, each search result being associated with the a feature vector of j features;

access a conditioned ranking model , wherein the conditioned ranking model comprises;

for each i condition, j scoring functions corresponding to j features of the feature vector associated with each search result; and

a ranking algorithm for determining a rank of each search result;

calculate a score for each search result based at least in part on the i conditions and the j features as associated with each i condition;

receive, from the first user, a selection of one of the search results; and

modify one or more of the j scoring functions of the conditioned ranking model based at least in part on the selected search result by the first user.

20. 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;

receive a query from a first user;

parse the received query to identify i conditions associated with the received query;

identify one or more search results substantially matching the i conditions associated with the received query, each search result being associated with the a feature vector of j features;

access a conditioned ranking model , wherein the conditioned ranking model comprises;

for each i condition, j scoring functions corresponding to j features of the feature vector associated with each search result; and

a ranking algorithm for determining a rank of each search result;

calculate a score for each search result based at least in part on the i conditions and the j features as associated with each i condition;

receive, from the first user, a selection of one of the search results; and

modify one or more of the j scoring functions of the conditioned ranking model based at least in part on the selected search result by the first user.