System, Process and Software Arrangement for Providing Multidimensional Recommendations/Suggestions

• US 20120303569A1
• Filed: 08/03/2012
• Published: 11/29/2012
• Est. Priority Date: 02/12/2001
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Alexander Tuzhilin, Gediminas Adomavicius
• Inventors: TUZHILIN, Alexander, ADOMAVICIUS, Gediminas
• Granted Patent: US 8,924,264 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 16/244 Grouping and aggregation
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Claims

1-20. -20. (canceled)

21. A method, comprising:

receiving, at one or more computer servers, requests sent using an API for multidimensional data, the multidimensional data defining a multidimensional space having at least three dimensions, each dimension having one or more attributes characterizing each element of the dimension, each dimension being capable of providing variable information, wherein at least one dimension corresponds to a user profile;

retrieving, by one or more processors associated with the one or more computer servers, information from the multidimensional space;

generating, by the one or more processors, at least one recommendation based on the retrieved information, wherein the at least one recommendation includes at least one multidimensional suggestion, the at least one multidimensional suggestion including the at least three dimensions, each of which is configured to include a plurality of further attributes, and wherein at least one of;

each of the dimensions is capable of having a type that is different from a type of another one of the dimensions, or at least one of the dimensions includes profiles; and

sending, by the one or more processors, using the API, the at least one recommendation in response to the request.

22. The method of claim 21, further comprising:

defining the API based on the at least three dimensions of the multidimensional space.

23. The method of claim 21, wherein the multidimensional data comprises data received from an external source or from a user.

24. The method of claim 23, wherein the multidimensional data further comprises data estimated based on the data received from an external source or from a user.

25. The method of claim 21, wherein the user profile has static characteristics.

26. The method of claim 25, wherein a user profile with static characteristics includes factual information relating to the respective dimension, the factual information comprising fields, variables, records, or any combination thereof.

27. The method of claim 25, wherein each static profile is recorded in a data storage arrangement as one record per each dimension.

28. The method of claim 21, wherein the user profile has dynamic characteristics.

29. The method of claim 28, wherein a user profile with dynamic characteristics includes at least one set of rules capturing behavior characteristics of a user.

30. The method of claim 28, wherein a user profile with dynamic characteristics includes at least one set of sequences capturing behavior characteristics of a user.

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

receive, at one or more computer servers, requests sent using an API for multidimensional data, the multidimensional data defining a multidimensional space having at least three dimensions, each dimension having one or more attributes characterizing each element of the dimension, each dimension being capable of providing variable information, wherein at least one dimension corresponds to a user profile;

retrieve, by one or more processors associated with the one or more computer servers, information from the multidimensional space;

generate, by the one or more processors, at least one recommendation based on the retrieved information, wherein the at least one recommendation includes at least one multidimensional suggestion, the at least one multidimensional suggestion including the at least three dimensions, each of which is configured to include a plurality of further attributes, and wherein at least one of;

each of the dimensions is capable of having a type that is different from a type of another one of the dimensions, or at least one of the dimensions includes profiles; and

send, by the one or more processors, using the API, the at least one recommendation in response to the request.

32. The media of claim 31, further comprising software that is operable when executed to:

define the API based on the at least three dimensions of the multidimensional space.

33. The media of claim 31, wherein the user profile has static characteristics including factual information relating to the respective dimension, the factual information comprising fields, variables, records, or any combination thereof.

34. The media of claim 31, wherein the user profile has static characteristics recorded in a data storage arrangement as one record per each dimension.

35. The media of claim 31, wherein the user profile has dynamic characteristics including at least one set of rules capturing behavior characteristics of a user.

36. The media of claim 31, wherein the user profile has dynamic characteristics including at least one set of sequences capturing behavior characteristics of a user.

37. A system comprising:

one or more processors associated with one or more computer servers; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

receive requests sent using an API for multidimensional data, the multidimensional data defining a multidimensional space having at least three dimensions, each dimension having one or more attributes characterizing each element of the dimension, each dimension being capable of providing variable information, wherein at least one dimension corresponds to a user profile;

retrieve information from the multidimensional space;

generate at least one recommendation based on the retrieved information, wherein the at least one recommendation includes at least one multidimensional suggestion, the at least one multidimensional suggestion including the at least three dimensions, each of which is configured to include a plurality of further attributes, and wherein at least one of;

each of the dimensions is capable of having a type that is different from a type of another one of the dimensions, or at least one of the dimensions includes profiles; and

send using the API, the at least one recommendation in response to the request.

38. The system of claim 37, wherein the processors are further operable when executing the instructions to:

define the API based on the at least three dimensions of the multidimensional space.

39. The system of claim 37, wherein the multidimensional data comprises data received from an external source or from a user.

40. The system of claim 37, wherein the multidimensional data further comprises data estimated based on the data received from an external source or from a user.