SYSTEM AND METHOD OF USING MOVIE TASTE FOR COMPATIBILITY MATCHING

• US 20100198773A1
• Filed: 11/06/2007
• Published: 08/05/2010
• Est. Priority Date: 11/06/2006
• Status: Abandoned Application

• Current Assignee: Promethean Ventures, LLC
• Original Assignee: Promethean Ventures, LLC
• Inventors: Wallisch, Pascal
• Application Number:
  US12/513,651
• Publication Number:
  US 20100198773A1
• Time in Patent Office: Days
• US Class Current: 706/54
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method of predicting the compatibility of at least one item of interest to a user of a web-based system. The method includes the steps of providing a survey of items for rating by the system user, collecting a set of ratings for the survey of items from the system user, collecting a set of ratings for the survey of items from each of a plurality of raters, calculating a correlation coefficient between the system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items, selecting a group of raters from the plurality of raters, the group of raters selected on the basis that each member of the group of raters has provided a rating of the at least one item of interest and predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the group of raters and the correlation coefficients calculated between the system user and each of the group of raters.

Claims

1. A method of predicting the compatibility of at least one item of interest to a user of a web-based system, comprising the steps of:

(a) providing a survey of items for rating by the system user;

(b) collecting a set of ratings for the survey of items from the system user;

(c) collecting a set of ratings for the survey of items from each of a plurality of raters;

(d) calculating a correlation coefficient between the system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items;

(e) selecting a group of raters from the plurality of raters, the group of raters selected on the basis that each member of the group of raters has provided a rating of the at least one item of interest; and

(f) predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the group of raters and the correlation coefficients calculated between the system user and each of the group of raters.

2. The method of claim 1, wherein the survey of items is a list of movies.

3. The method of claim 2, wherein the plurality of raters are selected from a list of system users.

4. The method of claim 2, wherein the plurality of raters are selected from a group of movie critics who have each rated the list of movies.

5. The method of claim 4, wherein the set of ratings for the survey of items is obtained using at least one web crawler.

6. The method of claim 1, wherein said step of calculating a correlation coefficient between the system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items comprises the steps of:

(i) obtaining a list of items from the survey of items that the system user and the plurality of raters have rated;

(ii) storing the list of items obtained in step (i) in a temporary list in the form of rating pairs;

(iii) calculating a mean user rating of survey items and a mean rater rating for each of the plurality of raters of survey items from the temporary list;

(iv) calculating the difference between each user rating from the survey of items and the mean user rating; and

(v) calculating the difference between each rater rating from the survey of items and the mean rater rating.

7. The method of claim 6, wherein said step of calculating a correlation coefficient between the system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items further comprises the steps of:

(vii) multiplying the difference between each user rating and the mean user rating and the difference between each rater rating and the mean rater rating for each movie rate;

(viii) summing the multiplied differences from step (vii) to determine a coefficient of variance;

(ix) dividing the sum obtained in step (viii) by the number of items on the temporary list to arrive at a mean coefficient of variance;

(x) calculating a first standard deviation for the system user ratings on the temporary list and a second standard deviation of the rater ratings on the temporary list;

(xi) calculating the product of the first and second standard deviations; and

(xii) dividing the mean coefficient of variance obtained in step (ix) by the product of the first and second standard deviations obtained in step (xi) to determine a correlation coefficient between a system user and a rater.

8. The method of claim 7, wherein said step of predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the group of raters and the correlation coefficients calculated between the system user and each of the group of raters comprises the steps of:

(i) selecting each system rater from the plurality of raters that has rated the item of interest;

(ii) selecting and storing the correlation coefficients determined in step (d) between the system user and the system raters selected in step (i);

(iii) raising each correlation coefficient to the power of k to obtain a weight;

(iv) calculating the sum of all weights;

(v) multiplying each weight by a corresponding rating and summing the product of each weight and corresponding rating to yield a raw score; and

(vi) dividing the raw score of step (v) by the sum of all weights determined in step (iv) to obtain an estimate of prediction of compatibility of the at least one item of interest to the system user.

9. The method of claim 8, wherein said step of predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the group of raters and the correlation coefficients calculated between the system user and each of the group of raters further comprises the steps of:

(vii) calculating an average item rating for the system user;

(viii) calculating an average item rating for each system rater selected in step (i);

(ix) calculating a correction factor by subtracting the average item rating determined in step (viii) from the average item rating determined in step (vii); and

(x) adding the correction factor determined in step (ix) to the estimate of rating prediction determined in step (vi) to obtain the prediction of compatibility of the at least one item of interest to the system user.

10. The method of claim 1, wherein said step of predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the group of raters and the correlation coefficients calculated between the system user and each of the group of raters comprises the steps of:

(i) selecting each system rater from the plurality of raters that has rated the item of interest;

(ii) selecting and storing the correlation coefficients determined in step (d) between the system user and the system raters selected in step (i);

(iii) raising each correlation coefficient to the power of k to obtain a weight;

(iv) calculating the sum of all weights;

(v) multiplying each weight by a corresponding rating and summing the product of each weight and corresponding rating to yield a raw score; and

(vi) dividing the raw score of step (v) by the sum of all weights determined in step (iv) to obtain an estimate of prediction of compatibility of the at least one item of interest to the system user.

11. The method of claim 10, wherein said step of predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the group of raters and the correlation coefficients calculated between the system user and each of the group of raters further comprises the steps of:

(vii) calculating an average item rating for the system user;

(viii) calculating an average item rating for each system rater selected in step (i);

(ix) calculating a correction factor by subtracting the average item rating determined in step (viii) from the average item rating determined in step (vii); and

(x) adding the correction factor determined in step (ix) to the estimate of rating prediction determined in step (vi) to obtain the prediction of compatibility of the at least one item of interest to the system user.

12. The method of claim 1, further comprising the step of:

(g) providing a list of items of interest to the system user and their corresponding predictions of compatibility over the mobile web to a web-enabled handheld device.

13. The method of claim 12, wherein the list of items of interest is a list of most recently released movies.

14. The method of claim 1, further comprising the step of:

(g) providing a list of items of interest to the system user and their corresponding predictions of compatibility over the cellular telephone network to a cellular telephone via text message.

15. The method of claim 14, wherein the list of items of interest is a list of most recently released movies.

16. In a web-based system, a method of predicting the compatibility of a first user of the system to at least one other user of the system, comprising the steps of:

(a) providing a survey of items for rating by the first system user;

(b) collecting a set of ratings for the survey of items from the first system user;

(c) collecting a set of ratings for the survey of items from each of a plurality of raters;

(d) calculating a correlation coefficient between the first system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items;

(e) predicting the compatibility of the first system user to at least one of the plurality of raters from the correlation coefficients calculated between the system user and each of the group of raters; and

(f) providing to the first system user at least one other user selected on the basis of correlation to the first system user from the plurality of raters.

17. The method of claim 16, wherein the survey of items is a list of movies.

18. The method of claim 17, wherein the plurality of raters are selected from a list of system users.

19. The method of claim 16, wherein said step of calculating a correlation coefficient between the system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items comprises the steps of:

(i) obtaining a list of items from the survey of items that the system user and the plurality of raters have rated;

(ii) storing the list of items obtained in step (i) in a temporary list in the form of rating pairs;

(iii) calculating a mean user rating of survey items and a mean rater rating for each of the plurality of raters of survey items from the temporary list;

(iv) calculating the difference between each user rating from the survey of items and the mean user rating; and

(v) calculating the difference between each rater rating from the survey of items and the mean rater rating.

20. The method of claim 19, wherein said step of calculating a correlation coefficient between the system user and each of the plurality of raters to obtain a set of correlation coefficients for the survey of items further comprises the steps of:

(vii) multiplying the difference between each user rating and the mean user rating and the difference between each rater rating and the mean rater rating for each movie rate;

(viii) summing the multiplied differences from step (vii) to determine a coefficient of variance;

(ix) dividing the sum obtained in step (viii) by the number of items on the temporary list to arrive at a mean coefficient of variance;

(x) calculating a first standard deviation for the system user ratings on the temporary list and a second standard deviation of the rater ratings on the temporary list;

(xi) calculating the product of the first and second standard deviations; and

(xii) dividing the mean coefficient of variance obtained in step (ix) by the product of the first and second standard deviations obtained in step (xi) to determine a correlation coefficient between a system user and a rater.

21. A multi-user web-based computer system for predicting the compatibility of at least one item of interest to a user of the system, comprising:

(a) a web server for communicating with users of the web-based computer system and components thereof;

(b) a user profile database for storing information on system users;

(c) a user rating module for providing a survey of items for rating by the system user and collecting a set of ratings for storage within said user profile database;

(d) a computational process module, said computational process module having a correlation module for calculating a correlation coefficient between the system user and each of a plurality of raters to obtain a set of correlation coefficients for the survey of items and a predictive module for predicting the compatibility of the at least one item of interest to the system user from the ratings provided by the plurality of raters and the correlation coefficients calculated between the system user and each of the plurality of raters;

(e) a compatibility-based matched items table for receiving an output regarding the compatibility of the at least one item of interest to the system user from the computational process module; and

(f) a recommendation process module for receiving information from said compatibility-based matched items table and returning the information to said web server for transmitting to the system user.

22. The web-based computer system of claim 21, wherein the survey of items is a list of movies.

23. The web-based computer system of claim 22, wherein the plurality of raters are selected from a list of system users.

24. The web-based computer system of claim 22, wherein the plurality of raters are selected from a group of movie critics who have each rated the list of movies.

25. The web-based computer system of claim 24, further comprising a web crawler for obtaining the set of ratings for the survey of items.

26. The web-based computer system of claim 21, wherein said correlation module of said computational process module comprises:

(i) means for obtaining a list of items from the survey of items that the system user and the plurality of raters have rated;

(ii) means for storing the list of items in a temporary list in the form of rating pairs;

(iii) means for calculating a mean user rating of survey items and a mean rater rating for each of the plurality of raters of survey items from the temporary list;

(iv) means for calculating the difference between each user rating from the survey of items and the mean user rating; and

(v) means for calculating the difference between each rater rating from the survey of items and the mean rater rating.

27. The web-based computer system of claim 26, wherein said correlation module of said computational process module further comprises:

(vii) means for multiplying the difference between each user rating and the mean user rating and the difference between each rater rating and the mean rater rating for each movie rate;

(viii) means for summing the multiplied differences from step (vii) to determine a coefficient of variance;

(ix) means for dividing the sum obtained said summing means by the number of items on the temporary list to arrive at a mean coefficient of variance;

(x) means for calculating a first standard deviation for the system user ratings on the temporary list and a second standard deviation of the rater ratings on the temporary list;

(xi) means for calculating the product of the first and second standard deviations; and

(xii) means for dividing the mean coefficient of variance by the product of the first and second standard deviations to determine a correlation coefficient between a system user and a rater.

28. The web-based computer system of claim 27, wherein said predictive module of said computational process module comprises:

(i) means for selecting each system rater from the plurality of raters that has rated the item of interest;

(ii) means for selecting and storing the correlation coefficients between the system user and the system raters selected by said means for selecting each system rater determined by said correlation module of said computational process module;

(iii) means for raising each correlation coefficient to the power of k to obtain a weight;

(iv) means for calculating the sum of all weights;

(v) means for multiplying each weight by a corresponding rating and summing the product of each weight and corresponding rating to yield a raw score; and

(vi) means for dividing the raw score by the sum of all weights determined to obtain an estimate of prediction of compatibility of the at least one item of interest to the system user.

29. The web-based computer system of claim 28, wherein said predictive module of said computational process module further comprises:

(vii) means for calculating an average item rating for the system user;

(viii) means for calculating an average item rating for each system rater selected by said means for selecting each system rater;

(ix) means for calculating a correction factor by subtracting the average item rating for each system rater from the average item rating for the system user; and

(x) means for adding the correction factor to the estimate of rating prediction to obtain the prediction of compatibility of the at least one item of interest to the system user.

30. The web-based computer system of claim 21, wherein said predictive module of said computational process module comprises:

(i) means for selecting each system rater from the plurality of raters that has rated the item of interest;

(ii) means for selecting and storing the correlation coefficients between the system user and the system raters selected by said means for selecting each system rater determined by said correlation module of said computational process module;

(iii) means for raising each correlation coefficient to the power of k to obtain a weight;

(iv) means for calculating the sum of all weights;

(v) means for multiplying each weight by a corresponding rating and summing the product of each weight and corresponding rating to yield a raw score; and

(vi) means for dividing the raw score by the sum of all weights determined to obtain an estimate of prediction of compatibility of the at least one item of interest to the system user.

31. The web-based computer system of claim 30, wherein said predictive module of said computational process module further comprises:

(vii) means for calculating an average item rating for the system user;

(viii) means for calculating an average item rating for each system rater selected by said means for selecting each system rater;

(ix) means for calculating a correction factor by subtracting the average item rating for each system rater from the average item rating for the system user; and

(x) means for adding the correction factor to the estimate of rating prediction to obtain the prediction of compatibility of the at least one item of interest to the system user.

32. The web-based computer system of claim 21, further comprising:

(g) means for providing a list of items of interest to the system user and their corresponding predictions of compatibility over the mobile web to a web-enabled handheld device.

33. The web-based computer system of claim 32, wherein the list of items of interest is a list of most recently released movies.

34. The web-based computer system of claim 21, further comprising:

(g) means for providing a list of items of interest to the system user and their corresponding predictions of compatibility over the cellular telephone network to a cellular telephone via text message.

35. The web-based computer system of claim 34, wherein the list of items of interest is a list of most recently released movies.