Correlated information recommendation

• US 8,655,949 B2
• Filed: 12/12/2012
• Issued: 02/18/2014
• Est. Priority Date: 07/24/2008
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Zhu, Baojin, Zhang, Qing
• Primary Examiner(s): Chan, Wing F
• Assistant Examiner(s): Woo, Andrew
• Application Number:
  US13/711,926
• Publication Number:
  US 20130110915A1
• Time in Patent Office: 433 Days
• Field of Search: 709/203, 709/201
• US Class Current: 709/203
• CPC Class Codes: G06F 16/2425 Iterative querying; Query formulation based on the results of a preceding query
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• 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 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/908 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/00 Network arrangements or protocols for supporting network services or applications user-to-user messaging H04L51/00; network arrangements, protocols or services for supporting real-time applications in data packet communications networks H04L65/00
• CPC Class Codes: H04L 67/01 Protocols

Abstract

Method and apparatus for information recommendation are provided. In one aspect, a method for information recommendation uses correlated information combinations to improve recommendation accuracy. Upon receiving data from a client indicating a visited information type, the method obtains correlated information types related to the visited information type from stored records. The correlated information types providing one or more correlated information combinations each including at least two correlated information types. For each of the one or more correlated information combinations, the method computes a degree of correlation between the correlated information combination and the visited information type. The method selects a target correlated information combination with a satisfying degree of correlation, and recommends the target correlated information combination to the client.

Claims

1. A method implemented by a computer for obtaining information recommendation, the method comprising:

receiving data from a client indicating a visited information type;

obtaining a plurality of correlated information types related to the visited information type from stored records, the plurality of correlated information types providing one or more correlated information combinations each including at least two correlated information types;

for each of the one or more correlated information combinations, computing a degree of correlation between the correlated information combination and the visited information type; and

selecting from the one or more correlated information combinations a target correlated information combination, and recommending the target correlated information combination to the client, wherein selecting the target correlated information combination comprises;

ranking the one or more correlated information combinations according to the degree of correlation of each of the one or more correlated information combinations,

comparing the degree of correlation of each of the one or more correlated information combinations with a set value, and

selecting a correlated information combination which degree of correlation is greater than the set value and is ranked the highest among the one or more correlated information combinations to be the target correlated information combination.

2. The method as recited in claim 1, wherein the degree of correlation between the correlated information combination and the visited information type is computed based on an occurrence property of the correlated information combination.

3. The method as recited in any one of claim 2, wherein the occurrence property comprises one or more of a frequency of occurrence, a time of occurrence, or a platform of occurrence.

4. The method as recited in claim 1, wherein the visited information type includes one or more of merchandise information, blog information, group information, post information, product information, news information, message information, keyword information, or advertisement information.

5. The method as recited in claim 1, wherein computing the degree of correlation between each of the one or more correlated information combinations and the visited information type comprises:

selecting the one or more correlated information combinations based on an application scenario of the client;

determining an occurrence property of each of the one or more correlated information combinations; and

computing the degree of correlation between each of the one or more correlated information combinations and the visited information type using the occurrence property.

6. The method as recited in claim 1, wherein obtaining the plurality of correlated information types related to the visited information type comprises:

obtaining from the stored records one or more other clients which have used the visited information type; and

obtaining one or more other information types which have been used by the one or more other clients from the stored records.

7. The method as recited in claim 1, further comprising:

obtaining user information of the current client and user information of one or more other clients which have used the visited information type; and

obtaining one or more other information types which have been used by the one or more other clients from the stored records;

wherein computing the degree of correlation between each of the one or more correlated information combinations and the visited information type further comprises;

determining a weight coefficient for the correlated information combination according to the user information of the current client and the user information of the one or more other clients which have used the visited information type; and

computing the degree of correlation between the correlated information combination and the visited information type based on an occurrence property of the correlated information combination and the weight coefficient.

8. The method as recited in claim 7, wherein the user information of the current client and the user information of the one or more current clients each include one or more of user age information, user education level, user career information, and user family information.

9. The method as recited in claim 1, further comprising:

converting the target correlated information combination into a hypertext format; and

sending the hypertext format to the client.

10. An apparatus for information recommendation, the apparatus comprising:

one or more processors;

memory;

a receiving unit stored in the memory and executable by the one or more processors that is configured to obtain from stored records a plurality of correlated information types related to a visited information type indicated by a client, the plurality of correlated information types providing one or more correlated information combinations each including at least two correlated information types;

a computation unit stored in the memory and executable by the one or more processors that is configured to obtain an occurrence property of each of the one or more correlated information combinations, and compute a degree of correlation between each of the one or more correlated information combinations and the visited information type; and

a recommendation unit stored in the memory and executable by the one or more processors that is configured to select from the one or more correlated information combinations a target correlated information combination, and recommend the target correlated information combination to the client, wherein selecting the target correlated information combination comprises;

ranking the one or more correlated information combinations according to the degree of correlation of each of the one or more correlated information combinations,

comparing the degree of correlation of each of the one or more correlated information combinations with a set value, and

selecting a correlated information combination which degree of correlation is greater than the set value and is ranked the highest among the one or more correlated information combinations to be the target correlated information combination.

11. The apparatus as recited in claim 10, wherein the apparatus further comprises:

a storage unit configured to store the stored records containing information that have been used by the current client and one or more other clients.

12. The apparatus as recited in claim 10, wherein the computation unit is further configured to select the one or more correlated information combinations of at least two correlated information types based on an application scenario of the client.

13. The apparatus as recited in claim 10, wherein the apparatus is further adapted to obtain user information of the current client and user information of one or more other clients which have used the visited information type and obtaining one or more other information types which have been used by the other clients from the stored records, and wherein the computation unit is further configured to:

determine a weight coefficient for each of the one or more correlated information combinations according to the user information of the current client and the user information of the one or more other clients which have used the visited information type; and

compute the degree of correlation between each of the one or more correlated information combinations and the visited information type based on an occurrence property of the respective correlated information combination and the weight coefficient.

14. The apparatus as recited in claim 10, wherein the apparatus comprises a server computer.

15. One or more memory devices storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:

obtaining from stored records a plurality of correlated information types related to a visited information type indicated by a client, the plurality of correlated information types providing one or more correlated information combinations each including at least two correlated information types;

obtaining an occurrence property of each of the one or more correlated information combinations, and compute a degree of correlation between each of the one or more correlated information combinations and the visited information type; and

selecting from the one or more correlated information combinations a target correlated information combination; and

recommending the target correlated information combination to the client, wherein selecting the target correlated information combination comprises;

ranking the one or more correlated information combinations according to the degree of correlation of each of the one or more correlated information combinations,

comparing the degree of correlation of each of the one or more correlated information combinations with a set value, and

selecting a correlated information combination which degree of correlation is greater than the set value and is ranked the highest among the one or more correlated information combinations to be the target correlated information combination.

16. The one or more memory devices as recited in claim 15, the acts further comprising storing the stored records containing information that have been used by the current client and one or more other clients.

17. The one or more memory devices as recited in claim 15, the acts further comprising selecting the one or more correlated information combinations of at least two correlated information types based on an application scenario of the client.

18. The one or more memory devices as recited in claim 15, the acts further comprising:

obtaining user information of the current client and user information of one or more other clients which have used the visited information type;

obtaining one or more other information types which have been used by the other clients from the stored records;

determining a weight coefficient for each of the one or more correlated information combinations according to the user information of the current client and the user information of the one or more other clients which have used the visited information type; and

computing the degree of correlation between each of the one or more correlated information combinations and the visited information type based on an occurrence property of the respective correlated information combination and the weight coefficient.