Feature Discretization and Cardinality Reduction Using Collaborative Filtering Techniques

• US 20090077093A1
• Filed: 09/19/2007
• Published: 03/19/2009
• Est. Priority Date: 09/19/2007
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Wang, Chao, Sarma, Joydeep Sen
• Application Number:
  US11/857,787
• Publication Number:
  US 20090077093A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/217 Database tuning G06F16/2282 takes precedence; database performance monitoring G06F11/3409

Abstract

A system and method to perform discretization and cardinality reduction of item attributes using collaborative filtering techniques are described. Data input by a user is received over a network, the input data further including a plurality of items and associated item metadata related to events performed by the user. The input data is further processed to obtain a predetermined number of groupings, each grouping having a calculated value based on a distance parameter between corresponding attributes of each item stored within the item metadata. Finally, a similarity parameter is computed between each pair of items within the plurality of items based on associated groupings and recommendations of the items are presented to the user based on the corresponding calculated similarity parameter.

Claims

1. A method comprising:

receiving data input by a user over a network, said input data further comprising a plurality of items and associated item metadata related to events performed by said user; and

processing said input data to obtain a predetermined number of groupings, each grouping having a calculated value based on a distance parameter between corresponding attributes of each item stored within said item metadata.

2. The method according to claim 1, further comprising:

computing a similarity parameter between each pair of items within said plurality of items based on associated groupings within said predetermined number of groupings; and

presenting recommendations of said items to said user based on said corresponding calculated similarity parameter.

3. The method according to claim 1, further comprising:

storing said events within a data storage device; and

storing said item metadata within said data storage device, said item metadata further comprising said corresponding attributes of said each item.

4. The method according to claim 1, further comprising:

determining a minimum value and a maximum value corresponding to each attribute;

defining a predetermined range of attribute values between said minimum value and said maximum value of said each attribute;

storing attribute-value pairs within attribute tables of a data storage device in connection with said associated user; and

calculating said distance parameter based on said stored attribute-value pairs using a cosine dot product function.

5. The method according to claim 4, further comprising:

successively retrieving two adjacent attribute-value pairs having highest respective distance parameter values from said attribute tables;

combining said adjacent attribute-value pairs into a resulting value;

storing said resulting value within said data storage device.

6. A computer readable medium containing executable instructions, which, when executed in a processing system, cause said processing system to perform a method comprising:

receiving data input by a user over a network, said input data further comprising a plurality of items and associated item metadata related to events performed by said user; and

processing said input data to obtain a predetermined number of groupings, each grouping having a calculated value based on a distance parameter between corresponding attributes of each item stored within said item metadata.

7. The computer readable medium according to claim 6, wherein said method further comprises:

computing a similarity parameter between each pair of items within said plurality of items based on associated groupings within said predetermined number of groupings; and

presenting recommendations of said items to said user based on said corresponding calculated similarity parameter.

8. The computer readable medium according to claim 6, wherein said method further comprises:

storing said events within a data storage device; and

storing said item metadata within said data storage device, said item metadata further comprising said corresponding attributes of said each item.

9. The computer readable medium according to claim 6, wherein said method further comprises:

determining a minimum value and a maximum value corresponding to each attribute;

defining a predetermined range of attribute values between said minimum value and said maximum value of said each attribute;

storing attribute-value pairs within attribute tables of a data storage device in connection with said associated user; and

calculating said distance parameter based on said stored attribute-value pairs using a cosine dot product function.

10. The computer readable medium according to claim 9, wherein said method further comprises:

successively retrieving two adjacent attribute-value pairs having highest respective distance parameter values from said attribute tables;

combining said adjacent attribute-value pairs into a resulting value;

storing said resulting value within said data storage device.

11. A system comprising:

at least one web server to receive data input by a user over a network, said input data further comprising a plurality of items and associated item metadata related to events performed by said user; and

a processing engine coupled to said at least one web server to process said input data to obtain a predetermined number of groupings, each grouping having a calculated value based on a distance parameter between corresponding attributes of each item stored within said item metadata.