Ranking recommended search queries

• US 10,360,227 B2
• Filed: 07/28/2016
• Issued: 07/23/2019
• Est. Priority Date: 12/19/2013
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Garg, Avichal, Hua, Ming, Vernal, Michael S., Qin, Yang, Fechete, Dan Ionut
• Primary Examiner(s): Le, Uyen T
• Application Number:
  US15/222,072
• Publication Number:
  US 20160335270A1
• Time in Patent Office: 1,090 Days
• Field of Search: None
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/332 Query formulation
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/3328 using graphical result space presentation or visualisation
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

In one embodiment, a method includes receiving, from a client system of a first user of the communication system, an input from the first user to access a card-stack interface, generating a card cluster comprising a plurality of cards, each card comprising a suggested query referencing a query-domain and one or more query-filters, wherein each query-filter references one or more objects associated with the communication system, and wherein each card in the card cluster is ranked within the card cluster based on a predicted click-thru rate (CTR) for the card based on one or more user-engagement factors, and sending, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises the card cluster, the cards of the card cluster being ordered based on the rankings associated with the cards.

Claims

1. A method comprising, by a computing device of a communication system:

receiving, from a client system of a first user of the communication system, an input from the first user to access a card-stack interface;

generating a card cluster comprising a plurality of cards, each card comprising a suggested query referencing a query-domain and one or more query-filters, wherein each query-filter references one or more objects associated with the communication system, and wherein each card in the card cluster is ranked within the card cluster based on a predicted click-thru rate (CTR) for the card based on one or more user-engagement factors; and

sending, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises the card cluster, the cards of the card cluster being ordered based on the rankings associated with the cards.

2. The method of claim 1, further comprising:

calculating the predicted CTR for each card in the card cluster based on the one or more user-engagement factors.

3. The method of claim 2, wherein calculating the predicted CTR comprises determining a click-thru behavior of one or more second users with respect to each card, each second user having at least a subset of demographic information in common with demographic information of the first user.

4. The method of claim 2, wherein calculating the predicted CTR comprises determining whether the first user is a tourist with respect to the current location based on a comparison of the current location with demographic information of the first user.

5. The method of claim 2, wherein calculating the predicted CTR comprises calculating a card-affinity of the first user with respect to each card based on the current time of day.

6. The method of claim 2, wherein calculating the predicted CTR comprises calculating a card-affinity of the first user with respect to each card based on an affinity to the query-domain or query-filter of each card.

7. The method of claim 2, wherein calculating the predicted CTR comprises calculating a card-affinity based on an affinity of one or more second users to the query-domain or query-filter of each card, the second users having at least a subset of demographic information in common with demographic information of the first user.

8. The method of claim 1, further comprising:

ranking each of the cards in the card cluster based on the predicted CTR.

9. The method of claim 8, wherein ranking each of the cards in the card cluster is further based on an associated user-interest value associated with each card.

10. The method of claim 8, wherein ranking each of the cards in the card cluster comprises ranking each query-domain based on a highest predicted CTR of the cards referencing each query-domain.

11. The method of claim 8, wherein ranking each of the cards in the card cluster comprises ranking each query-domain based on a calculated average predicted CTR value of the cards referencing each query-domain.

12. The method of claim 8, wherein ranking each of the cards in the card cluster comprises ranking each query-domain based on a calculated weighted average predicted CTR value of the cards referencing each query-domain.

13. The method of claim 8, wherein ranking each of the cards in the card cluster comprises calculating a card-affinity of each card relative to an inferred intent of the first user.

14. The method of claim 1, further comprising:

displaying, at the client system, a subsequent card in the card cluster to the first user in response to detecting an input from the first user corresponding to a swipe of a previously displayed card, the subsequent card having a same query-domain and one or more different query-filters as the previously displayed card.

15. The method of claim 1, wherein the card-stack interface is displayed on a page currently accessed by the first user.

16. The method of claim 1, wherein the card-stack interface is displayed on a user interface of a native application installed on the client system that is associated with the communication system.

17. The method of claim 1, wherein the card cluster is vertically displayed within the card-stack interface, and wherein each displayed card references a different query-domain.

18. The method of claim 1, further comprising:

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 node corresponding to the first user; and

a plurality of second nodes corresponding to a plurality of objects associated with the communication system, respectively,

wherein each query-filter references one or more nodes of the plurality of nodes or one or more edges of the plurality of edges.

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

receive, from a client system of a first user of the communication system, an input from the first user to access a card-stack interface;

generate a card cluster comprising a plurality of cards, each card comprising a suggested query referencing a query-domain and one or more query-filters, wherein each query-filter references one or more objects associated with the communication system, and wherein each card in the card cluster is ranked within the card cluster based on a predicted click-thru rate (CTR) for the card based on one or more user-engagement factors; and

send, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises the card cluster, the cards of the card cluster being ordered based on the rankings associated with the cards.

20. A communication 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, from a client system of a first user of the communication system, an input from the first user to access a card-stack interface;

generate a card cluster comprising a plurality of cards, each card comprising a suggested query referencing a query-domain and one or more query-filters, wherein each query-filter references one or more objects associated with the communication system, and wherein each card in the card cluster is ranked within the card cluster based on a predicted click-thru rate (CTR) for the card based on one or more user-engagement factors; and

send, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises the card cluster, the cards of the card cluster being ordered based on the rankings associated with the cards.