APPROACH FOR ESTIMATING USER RATINGS OF ITEMS

• US 20100010877A1
• Filed: 09/18/2009
• Published: 01/14/2010
• Est. Priority Date: 02/06/2004
• Status: Active Grant

• Current Assignee: Hastings W. Reed, John Robert Ciancutti, Lawrence Wen-Kai Shih, Neil Duncan Hunt, Shawn Michael Purcell, Stanley Miguel Lanning
• Original Assignee: Hastings W. Reed, John Robert Ciancutti, Lawrence Wen-Kai Shih, Neil Duncan Hunt, Shawn Michael Purcell, Stanley Miguel Lanning
• Inventors: Shih, Lawrence Wen-Kai, Lanning, Stanley Miguel, Purcell, Shawn Michael, Hunt, Neil Duncan, Ciancutti, John Robert, Hastings, W. Reed
• Granted Patent: US 8,271,338 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0217 involving input on products or services in exchange for incentives or rewards
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0623 Item investigation
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

According to an embodiment of the invention, an approach is provided for estimating how a particular user would rate a particular item from a plurality of items. The approach is applicable to any type of items, including rental items such as movies, music and games, and the invention is not limited to any particular type of item. One or more items from the plurality of items that have ratings similar to the particular item are identified. One or more other users are identified that have given ratings to the one or more items that are substantially similar to ratings given by the particular user to the one or more items. An estimation is made of how the particular user would rate the particular item based upon ratings for the particular item given by the one or more other users.

Claims

1. A method for estimating how a particular user of a plurality of users would rate a particular item of a plurality of items, the method comprising:

maintaining ratings data that represents ratings given to a plurality of items by a plurality of users, wherein the ratings data indicates that a particular user of the plurality of users has not yet given a rating to a particular item of the plurality of items;

identifying, from the plurality of items, one or more reference items that have been given ratings similar to ratings given to the particular item that the particular user has not yet rated;

identifying one or more other users of the plurality of users that have given ratings to the one or more reference items that are substantially similar to ratings given by the particular user to the one or more reference items;

generating an estimation of how the particular user would rate the particular item based upon ratings given to the particular item by the one or more other users;

wherein the method is performed by one or more computing devices.

2. The method of claim 1, further comprising:

selecting one or more reference users from the plurality of users based upon user selection criteria;

wherein identifying one or more other users of the plurality of users comprises identifying, from the one or more reference users, one or more other users of that have given ratings to the one or more reference items that are substantially similar to ratings given by the particular user to the one or more reference items.

3. The method of claim 2, wherein the user selection criteria comprise a minimum number of ratings and wherein selecting one or more reference users comprises selecting one or more reference users that have each given at least the minimum number of ratings.

4. The method of claim 2, wherein the user selection criteria comprise a maximum number of ratings and wherein selecting one or more reference users comprises selecting one or more reference users that have each given at most the maximum number of ratings.

5. The method of claim 2, wherein the user selection criteria comprise an average rating range and wherein selecting one or more reference users comprises selecting one or more reference users that have each given ratings, an average of which is within the average rating range.

6. The method of claim 2, wherein the user selection criteria comprise a tolerance of a normal distribution and wherein selecting one or more reference users comprises selecting one or more reference users that have each given ratings, a distribution of which is within the tolerance.

7. The method of claim 2, wherein the user selection criteria comprise a range of a standard deviation and wherein selecting one or more reference users comprises selecting one or more reference users that have each given ratings, a standard deviation of which is within the range.

8. The method of claim 1, wherein the ratings data is maintained in an array data structure in a volatile memory of a computing device, the array having a plurality of cells, each cell corresponding to a single item of the plurality of items and a single user of the plurality of users.

9. The method of claim 8, wherein at least one cell of the array comprises data representing a rating given by the user corresponding to the cell to the item corresponding to the cell and at least one other cell of the array comprises data representing an estimation of how the user corresponding to the other cell would rate the item corresponding to the other cell.

10. The method of claim 1, wherein said ratings data indicates that a second particular user of the plurality of users has not yet given a rating to a second particular item of the plurality of items, the method further comprising:

identifying, from the plurality of items, one or more similar items that are similar to the second particular item;

identifying a second set of one or more other users of the plurality of users that have given ratings to the one or more similar items that are substantially similar to ratings given by the second particular user to the one or more similar items;

generating an estimation of how the second particular user would rate the second particular item based upon ratings given to the second particular item by the second set of one or more other users;

wherein identifying, from the plurality of items, one or more similar items that are similar to the second particular item comprises;

associating semantic feature values with the plurality of items, wherein the semantic feature values are classified according to one or more semantic feature value types, wherein each feature value type is associated with a match score;

generating a similarly score for a candidate item of the plurality of items based on at least one match score and a number of matches between semantic feature values associated with the candidate item and semantic feature values associated with the second particular item.

11. A computer-readable storage medium storing instructions for estimating how a particular user of a plurality of users would rate a particular item of a plurality of items, the instructions, when executed by one or more computing devices, cause the one or more computing devices to perform:

maintaining ratings data that represents ratings given to a plurality of items by a plurality of users, wherein the ratings data indicates that a particular user of the plurality of users has not yet given a rating to a particular item of the plurality of items;

identifying, from the plurality of items, one or more reference items that have been given ratings similar to ratings given to the particular item that the particular user has not yet rated;

identifying one or more other users of the plurality of users that have given ratings to the one or more reference items that are substantially similar to ratings given by the particular user to the one or more reference items;

generating an estimation of how the particular user would rate the particular item based upon ratings given to the particular item by the one or more other users;

wherein the method is performed by one or more computing devices.

12. The computer-readable storage medium of claim 11, further comprising instructions which, when executed by the one or more computing devices, cause the one or more computing devices to perform:

selecting one or more reference users from the plurality of users based upon user selection criteria;

wherein identifying one or more other users of the plurality of users comprises identifying, from the one or more reference users, one or more other users of that have given ratings to the one or more reference items that are substantially similar to ratings given by the particular user to the one or more reference items.

13. The computer-readable storage medium of claim 12, wherein the user selection criteria comprise a minimum number of ratings and wherein selecting one or more reference users comprises selecting one or more reference users that have each given at least the minimum number of ratings.

14. The computer-readable storage medium of claim 12, wherein the user selection criteria comprise a maximum number of ratings and wherein selecting one or more reference users comprises selecting one or more reference users that have each given at most the maximum number of ratings.

15. The computer-readable storage medium of claim 12, wherein the user selection criteria comprise an average rating range and wherein selecting one or more reference users comprises selecting one or more reference users that have each given ratings, an average of which is within the average rating range.

16. The computer-readable storage medium of claim 12, wherein the user selection criteria comprise a tolerance of a normal distribution and wherein selecting one or more reference users comprises selecting one or more reference users that have each given ratings, a distribution of which is within the tolerance.

17. The computer-readable storage medium of claim 12, wherein the user selection criteria comprise a range of a standard deviation and wherein selecting one or more reference users comprises selecting one or more reference users that have each given ratings, a standard deviation of which is within the range.

18. The computer-readable storage medium of claim 11, wherein the ratings data is maintained in an array data structure in a volatile memory of a computing device, the array having a plurality of cells, each cell corresponding to a single item of the plurality of items and a single user of the plurality of users.

19. The computer-readable storage medium of claim 18, wherein at least one cell of the array comprises data representing a rating given by the user corresponding to the cell to the item corresponding to the cell and at least one other cell of the array comprises data representing an estimation of how the user corresponding to the other cell would rate the item corresponding to the other cell.

20. The computer-readable storage medium of claim 11, wherein said ratings data indicates that a second particular user of the plurality of users has not yet given a rating to a second particular item of the plurality of items, the computer-readable storage medium further comprising instructions which, when executed by the one or more computing devices, cause the one or more computing devices to perform:

identifying, from the plurality of items, one or more similar items that are similar to the second particular item;

identifying a second set of one or more other users of the plurality of users that have given ratings to the one or more similar items that are substantially similar to ratings given by the second particular user to the one or more similar items;

generating an estimation of how the second particular user would rate the second particular item based upon ratings given to the second particular item by the second set of one or more other users;

wherein identifying, from the plurality of items, one or more similar items that are similar to the second particular item comprises;

associating semantic feature values with the plurality of items, wherein the semantic feature values are classified according to one or more semantic feature value types, wherein each feature value type is associated with a match score;

generating a similarly score for a candidate item of the plurality of items based on at least one match score and a number of matches between semantic feature values associated with the candidate item and semantic feature values associated with the second particular item.

21. A method for estimating how a particular user of a plurality of users would rate a particular item of a plurality of items, the method comprising:

maintaining ratings data that represents ratings given to a plurality of items by a plurality of users, wherein the ratings data indicates that a particular user of the plurality of users has not yet given a rating to a particular item of the plurality of items;

identifying, from the plurality of items, one or more similar items that are similar to the particular item;

identifying one or more other users of the plurality of users that have given ratings to the one or more similar items that are substantially similar to ratings given by the particular user to the one or more similar items;

generating an estimation of how the particular user would rate the particular item based upon ratings given to the particular item by the one or more other users;

wherein identifying, from the plurality of items, one or more similar items that are similar to the particular item comprises;

associating semantic feature values with the plurality of items, wherein the semantic feature values are classified according to one or more semantic feature value types, wherein each feature value type is associated with a match score;

generating a similarly score for a candidate item of the plurality of items based on at least one match score and a number of matches between semantic feature values associated with the candidate item and semantic feature values associated with the particular item;

wherein the method is performed by one or more computing devices.

22. The method of claim 21, wherein each feature value type is further associated with a mismatch-score, and wherein generating a similarity score for the candidate item is further based on at least one mismatch score and a number of mismatches between semantic feature values associated with the candidate item and semantic feature values associated with the particular item.

23. The method of claim 21, wherein said ratings data indicates that a second particular user of the plurality of users has not yet given a rating to a second particular item of the plurality of items, the method further comprising:

identifying, from the plurality of items, one or more reference items that have been given ratings similar to ratings given to the second particular item that the second particular user has not yet rated;

identifying a second set of one or more other users of the plurality of users that have given ratings to the one or more reference items that are substantially similar to ratings given by the second particular user to the second set of one or more reference items;

generating an estimation of how the second particular user would rate the second particular item based upon ratings given to the second particular item by the second set of one or more other users.

25. The computer-readable storage medium of claim 23, wherein each feature value type is further associated with a mismatch-score, and wherein generating a similarity score for the candidate item is further based on at least one mismatch score and a number of mismatches between semantic feature values associated with the candidate item and semantic feature values associated with the particular item.

24. A computer-readable storage medium storing instructions for estimating how a particular user of a plurality of users would rate a particular item of a plurality of items, the instructions, when executed by one or more computing devices, cause the one or more computing devices to perform:

maintaining ratings data that represents ratings given to a plurality of items by a plurality of users, wherein the ratings data indicates that a particular user of the plurality of users has not yet given a rating to a particular item of the plurality of items;

identifying, from the plurality of items, one or more similar items that are similar to the particular item;

identifying one or more other users of the plurality of users that have given ratings to the one or more similar items that are substantially similar to ratings given by the particular user to the one or more similar items;

generating an estimation of how the particular user would rate the particular item based upon ratings given to the particular item by the one or more other users;

wherein identifying, from the plurality of items, one or more similar items that are similar to the particular item comprises;

associating semantic feature values with the plurality of items, wherein the semantic feature values are classified according to one or more semantic feature value types, wherein each feature value type is associated with a match score;

generating a similarly score for a candidate item of the plurality of items based on at least one match score and a number of matches between semantic feature values associated with the candidate item and semantic feature values associated with the particular item.

26. The computer-readable storage medium of claim 24, wherein said ratings data indicates that a second particular user of the plurality of users has not yet given a rating to a second particular item of the plurality of items, the computer-readable storage medium further comprising instructions which, when executed by the one or more computing devices, cause the one or more computing devices to perform:

identifying, from the plurality of items, one or more reference items that have been given ratings similar to ratings given to the second particular item that the second particular user has not yet rated;

identifying a second set of one or more other users of the plurality of users that have given ratings to the one or more reference items that are substantially similar to ratings given by the second particular user to the second set of one or more reference items;

generating an estimation of how the second particular user would rate the second particular item based upon ratings given to the second particular item by the second set of one or more other users.