RECOMMENDING RESOURCES

• US 20130007238A1
• Filed: 06/30/2011
• Published: 01/03/2013
• Est. Priority Date: 06/30/2011
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Sandholm, Thomas E.
• Application Number:
  US13/173,087
• Publication Number:
  US 20130007238A1
• Time in Patent Office: Days
• US Class Current: 709/223
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries

Abstract

A subset of identifiers for resources is selected from a collection of identifiers for resources based on values associated with the different identifiers. Pairwise similarity scores between different pairs of identifiers in the subset then are computed. Based on these computed pairwise similarity scores, another subset of identifiers is identified from within the initial subset of identifiers as corresponding to resources perceived as potentially being of interest to a user for recommendation to the user.

Claims

1. A system for providing a web page recommendation service comprising:

one or more processing elements; and

a computer memory storage system storing instructions that, when executed by the one or more processing elements, cause the one or more processing elements to;

access a database storing network addresses for web pages and corresponding indications of feedback generated by users of the web page recommendation service;

identify, from among the network addresses stored in the database, at least some network addresses as being relevant to a context of a particular user;

determine, based on indications of user-generated feedback corresponding to the network addresses identified as being relevant to the context of the particular user, scores for each of the network addresses identified as being relevant to the context of the particular user;

select, from the network addresses identified as being relevant to the context of the particular user and based on the scores determined for the network addresses identified as being relevant to the context of the particular user, a first subset of less than all of the network addresses identified as being relevant to the context of the particular user;

pseudorandomly select, from the first subset of network addresses, a second subset of less than all of the first subset of network addresses;

generate a network address similarity matrix specifying measures of pairwise similarity between network addresses of the second subset of network addresses;

determine if any indications of feedback generated by the particular user are stored in the database for the network addresses of the second subset of network addresses;

compute measures of perceived interest of the particular user in network addresses of the second subset of network addresses based on the network address similarity matrix and any indications of feedback generated by the particular user for the network addresses of the second subset of network addresses determined to be stored in the database;

identify, from the second subset of network addresses, network addresses for which no indications of feedback generated by the particular user are stored in the database;

select, from the network addresses identified as having no indications of feedback generated by the particular user stored in the database, a third subset of network addresses to recommend to the particular user based on the computed measures of perceived interest of the particular user in the network addresses for which no indications of feedback generated by the particular user are stored in the database; and

make the network addresses of the third subset available to the particular user.

2. The method of claim 1 wherein:

the instructions that, when executed by the one or more processing elements, cause the one or more processing elements to determine if any indications of feedback generated by the particular user are stored in the database for the network addresses of the second subset of network addresses include instructions that, when executed by the one or more processing elements, cause the one or more processing elements to determine that no indications of feedback generated by the particular user are stored in the database for the network addresses of the second subset of network addresses; and

the instructions that, when executed by the one or more processing elements, cause the one or more processing elements to compute measures of perceived interest of the particular user in network addresses of the second subset of network addresses include instructions that, when executed by the one or more processing elements, cause the one or more processing elements to;

generate a proxy for feedback generated by the particular user for at least one of the network addresses of the second subset of network addresses as a consequence of having determined that no indications of feedback generated by the particular user are stored in the database for the network addresses of the second subset of network addresses; and

compute measures of perceived interest of the particular user in network addresses of the second subset of network addresses based on the network address similarity matrix and the proxy for feedback generated by the particular user for the at least one network address of the second subset of network addresses.

3. A computer-implemented method comprising:

accessing, from a computer memory storage system, a collection of identifiers for resources, each identifier being associated with a user feedback score;

based on the user feedback scores associated with the identifiers, selecting, from the collection of identifiers and using a processing element, a first subset of unique identifiers that includes less than all of the identifiers from the collection of identifiers;

pseudorandomly selecting, from the first subset of identifiers and using a processing element, a second subset of less than all of the first subset of identifiers;

computing, using a processing element, pairwise similarity scores between different pairs of identifiers in the second subset of identifiers;

based on the computed pairwise similarity scores, identifying, from within the second subset of identifiers and using a processing element, a third subset of less than all of the second subset of identifiers corresponding to resources perceived as potentially being of interest to a user for recommendation to the user; and

making the third subset of identifiers available to the user.

4. The method of claim 3 wherein the resources are web pages and the identifiers for the resources are network addresses for the web pages such that:

accessing a collection of identifiers for resources comprises accessing a collection of network addresses for web pages, where each network address is associated with a user feedback score;

selecting, from the collection of identifiers, a first subset of unique identifiers based on the user feedback scores associated with the identifiers comprises selecting, from the collection of network addresses for web pages, a first subset of unique network addresses based on the user feedback scores associated with the network addresses;

pseudorandomly selecting, from the first subset of identifiers, a second subset of less than all of the first subset of identifiers comprises pseudorandomly selecting, from the first subset of network addresses, a second subset of less than all of the first subset of network addresses;

computing pairwise similarity scores between different pairs of identifiers in the second subset of identifiers comprises computing pairwise similarity scores between different pairs of network addresses in the second subset of network addresses;

identifying, from within the second subset of identifiers and based on the computed pairwise similarity scores, a third subset of less than all of the second subset of identifiers corresponding to resources perceived as potentially being of interest to a user comprises identifying, from within the second subset of network addresses and based on the computed pairwise similarity scores, a third subset of less than all of the second subset of network addresses corresponding to web pages perceived as potentially being of interest to a user; and

making the third subset of identifiers available to the user comprises making the third subset of network addresses available to the user.

5. The method of claim 4 wherein

the collection of network addresses for web pages includes multiple instances of the same network addresses, where each instance of a network address is associated with an individualized user feedback score;

selecting, from the collection of network addresses for web pages, a first subset of unique network addresses based on the user feedback scores associated with the network addresses includes;

computing, for each of at least some of the network addresses for which multiple instances are included in the collection of network addresses, an aggregate user feedback score based on individualized user feedback scores associated with at least some of the instances of the network address, and

selecting the first subset of unique network addresses based, at least in part, on the aggregate user feedback scores computed for network addresses for which multiple instances are included in the collection of network addresses.

6. The method of claim 5 wherein:

selecting, from the collection of network addresses for web pages, a first subset of unique network addresses includes selecting some number S x/of unique network addresses, where S represents the number of network addresses to be included in the second subset of network addresses and I>

1; and

pseudorandomly selecting, from the first subset of network addresses, a second subset of less than all of the first subset of network addresses includes pseudorandomly selecting S network addresses from the first subset of network addresses.

7. The method of claim 4 wherein selecting, from the collection of network addresses for web pages, a first subset of less than all of the network addresses based on the user feedback scores associated with the network addresses includes:

computing time-decayed user feedback scores based on times associated with user feedback scores, and

selecting the first subset of less than all of the network addresses based on the computed time-decayed user feedback scores.

8. The method of claim 4 wherein identifying, from within the second subset of network addresses and based on the computed pairwise similarity scores, the third subset of less than all of the second subset of network addresses corresponding to web pages perceived as potentially being of interest to the user includes:

computing, based on the pairwise similarity scores and individual user feedback scores for network addresses in the second subset attributable to the user, measures of perceived interest of the user in web pages corresponding to network addresses in the second subset of network addresses; and

identifying, from within the second subset of network addresses, the third subset of network addresses based on the computed measures of perceived interest of the user in web pages corresponding to network addresses in the second subset of network addresses.

9. The method of claim 4 further comprising identifying, from within the second subset of network addresses, those network addresses for which the collection of network addresses includes no individual user feedback scores attributable to the user, wherein identifying, from within the second subset of network addresses, the third subset of network addresses based on the computed measures of perceived interest of the user in web pages corresponding to network addresses in the second subset of network addresses includes selecting, from among the identified network addresses of the second subset for which the collection of network addresses includes no individual rating values attributable to the user, the third subset of network addresses based on the computed measures of perceived interest of the user in web pages corresponding to network addresses in the second subset of network addresses.

10. The method of claim 4 further comprising receiving an indication of a location of interest to the user, wherein:

the collection of network addresses is indexed according to locations associated with the web pages that correspond to the network addresses; and

selecting, from the collection of network addresses, the first subset of unique network addresses based on the user feedback scores associated with the network addresses includes;

filtering the indexed collection of network addresses into a filtered collection of network addresses that are associated with locations within a defined vicinity of the location of interest to the user, and

selecting the first subset of network addresses from the filtered collection of network addresses based on the user feedback scores associated with the network addresses.

11. The method of claim 4 further comprising receiving an indication of a category of interest to the user, wherein:

the collection of network addresses is indexed according to categories associated with the web pages that correspond to the network addresses; and

selecting, from the collection of network addresses, the first subset of unique network addresses based on the user feedback scores associated with the network addresses includes;

filtering the indexed collection of network addresses into a filtered collection of network addresses that are associated with the category of interest to the user, and

selecting the first subset of network addresses from the filtered collection of network addresses based on the user feedback scores associated with the network addresses.

12. The method of claim 4 further comprising receiving an indication of a set of user identifiers corresponding to co-users of interest to the user, wherein:

individual network addresses within the collection of network addresses are associated with individual user feedback scores attributable to individual users who to are identified by user identifiers;

the collection of network addresses is indexed according to the user identifiers that correspond to the users to whom individual user feedback scores associated with the network addresses are attributable; and

selecting, from the collection of network addresses, the first subset of unique network addresses based on the user feedback scores associated with the network addresses includes;

filtering the indexed collection of network addresses into a filtered collection of network addresses that are associated with individual user feedback scores attributable to individual users identified by user identifiers within the set of user identifiers that correspond to co-users of interest to the user, and

selecting the first subset of network addresses from the filtered collection of network addresses based on the user feedback scores associated with the network addresses.

13. The method of claim 3 wherein making the third subset of identifiers available to the user comprises transmitting the third subset of identifiers to a client device.

14. The method of claim 3 wherein making the third subset of identifiers available to the user comprises causing representations of the third subset of identifiers to be displayed on a display device.

15. A non-transitory computer-readable storage medium storing instructions for providing a web page recommendation service that, when executed by a computer, cause the computer to:

access, from a computer memory storage system, a collection of network addresses for web pages, each network address being associated with a user feedback score that is a function of at least one of a number of views by users of the web page recommendation service of the corresponding web page and user-provided rating information for the corresponding web page, at least some of the user feedback scores associated with network addresses in the collection being functions of both numbers of views by users of the web page recommendation service of their corresponding web pages and user-provided rating information for their corresponding web pages;

based on the user feedback scores associated with the network addresses, select, from the collection of network addresses, a first subset of less than all of the network addresses;

compute pairwise similarity scores between network addresses in the first subset of network addresses;

based on the computed pairwise similarity scores, identify, from within the first subset of network addresses, a second subset of less than all of the first subset of network addresses corresponding to web pages perceived as potentially being of interest to a user for recommendation to the user; and

make the second subset of network addresses available to the user.