Data Structures for Collaborative Filtering Systems

• US 20110231405A1
• Filed: 03/17/2010
• Published: 09/22/2011
• Est. Priority Date: 03/17/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Bachrach, Yoram, Herbrich, Ralf
• Granted Patent: US 8,560,528 B2
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata

Abstract

Data structures for collaborative filtering systems are described. In an embodiment sketches which extremely concisely represent a list of items that a user has rated are created and stored for use by a collaborative filtering system to recommend items. For example, the sketches are created by using several versions of a cryptographic hash function to permute the item list and store a minimal value from each permutation in the sketch together with a user rating. In examples the sketches are used to compute estimates of similarity measures between pairs of users such as rank correlations including Spearman'"'"'s Rho and Kendall'"'"'s Tau. For example, the similarity measures are used by a collaborative filtering system to accurately and efficiently recommend items to users. For example the sketches are so concise that massive amounts of data can be taken into account in order to give high quality recommendations in a practical manner.

Claims

1. A computer-implemented method of creating sketches for use by a collaborative filtering system for recommending items to users comprising:

accessing, for each user, an item list being a list of items that have been rated by that user;

creating and storing a sketch of each item list being a data structure holding a concise description of the item list;

arranging a sketch creation engine to create each sketch by using a hash function to generate a permutation of the item list, storing a minimum value of the permutation in the sketch, repeatedly generating permutations and storing a minimum value in the sketch for more hash functions, and also storing item ratings in the sketch such that there is one stored item rating associated with each stored minimum value.

2. A method as claimed in claim 1 wherein the hash functions are cryptographic hash functions.

3. A method as claimed in claim 1 wherein the hash functions are randomly selected from a min-wise independent family of hashes.

4. A method as claimed in claim 1 wherein the hash functions are keyed hash message authentication code versions of a cryptographic hash function.

5. A method as claimed in claim 1 which further comprises receiving a target user identifier, arranging a processor to identify pairs of users that include the target user and for each of those pairs computing an estimate of a rank correlation by using the sketches associated with the identified pairs of users.

6. A method as claimed in claim 5 which further comprises providing the rank correlation estimates to a collaborative filtering system and arranging that collaborative filtering system to generate recommendations of items for the target user identifier on the basis of the rank correlation estimates.

7. A method as claimed in claim 5 wherein the rank correlation is any rank correlation of the form

8. A method as claimed in claim 5 wherein the rank correlation is selected from any of Kendall'"'"'s Tau and Spearman'"'"'s Rho.

9. A method as claimed in claim 1 wherein the item lists are accessed from secondary storage or as a data stream and wherein the sketches are stored in RAM

10. A method at a collaborative filtering system of estimating a rank correlation between a pair of users comprising:

accessing a sketch for each user of the pair, each sketch being a data structure holding a description of a list of items rated by a user comprising a plurality of item identifiers and a rating for each item identifier;

each sketch being smaller than its associated item list;

arranging a processing to identify sketch collisions between the sketches where item identifiers at corresponding positions in the sketches are the same;

arranging the processor to examine the ratings of each item that occurs on a sketch collision and to use those ratings to compute an estimate of the rank correlation.

11. A method as claimed in claim 10 wherein the processor is arranged to compute an estimate of Spearman'"'"'s Rho by using the examined ratings to compute Spearman'"'"'s Rho random variables and combining those.

12. A method as claimed in claim 10 wherein the processor is arranged to find the proportion of sketch collisions which are agreements and use that proportion to compute an estimate of Kendall'"'"'s Tau.

13. A method as claimed in claim 12 wherein the processor is arranged to identify sketch collisions which are agreements by finding two adjacent collisions in a pair of sketches, for those collisions identifying the highest rating in each sketch, comparing the items having the identified ratings and identifying a sketch collisions agreement if the items are the same.

14. A collaborative filtering system comprising:

a memory holding a plurality of sketches, each sketch being a data structure holding a description of a list of items rated by a user comprising a plurality of item identifiers and a rating for each item identifier;

each sketch being smaller than its associated item list;

a processor arranged to identify sketch collisions between pairs of the sketches where item identifiers at corresponding positions in the sketches are the same;

the processor being arranged to examine the ratings of items that occur on sketch collisions and to use those ratings to compute estimates of a rank correlation between pairs of users;

the processor being arranged to predict the rating a target user would give to an unexamined item using at least some of the rank correlation estimates.

15. An apparatus as claimed in claim 14 wherein the processor is arranged to make the prediction using the ratings given to the unexamined item by other users, weighted according to the rank correlation estimates.

16. An apparatus as claimed in claim 14 wherein the processor is arranged to compute estimates of a rank correlation which is any rank correlation of the form

17. An apparatus as claimed in claim 14 wherein the processor is arranged to compute estimates of a rank correlation which is selected from any of Kendall'"'"'s Tau and Spearman'"'"'s Rho.

18. An apparatus as claimed in claim 14 further comprising a sketch creation engine arranged to create each sketch by using a hash function to generate a permutation of the item list, storing a minimum value of the permutation in the sketch, repeatedly generating permutations and storing a minimum value in the sketch for more hash functions, and also storing item ratings in the sketch such that there is one stored item rating associated with each stored minimum value.

19. An apparatus as claimed in claim 18 wherein the hash functions are keyed hash message authentication code versions of a cryptographic hash function.

20. An apparatus as claimed in claim 14 wherein the processor is arranged to generate recommendations of items for the target user using the predictions.