Recommending location and services via geospatial collaborative filtering

• US 20070005419A1
• Filed: 06/30/2005
• Published: 01/04/2007
• Est. Priority Date: 06/30/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric, Krumm, John
• Application Number:
  US11/171,940
• Publication Number:
  US 20070005419A1
• Time in Patent Office: Days
• US Class Current: 701/209
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

Geospatial collaborative filtering (CF) with spatial (or location) logs and location updates that facilitates recommending location and/or services information to an active user. A location tracking system tracks the user, and is employed in part to determine when the user is associated with the location, if the user pauses at the location and how long the user dwells at the location. Based in part on this data, collaborative filtering of data from others who have visited the location is applied to provide suggestions to the active user. Additionally, new information related to the location, nearby locations, and services can be presented to the user. The information can be related to businesses, weather conditions, what previous users have selected when at that location, and any amount of data desired to be accessed, for example.

Claims

1. A system that facilitates recommendation of location and services information, comprising:

a tracking component that tracks a location of a user;

a sensing component that senses interaction data of the user at the location;

a data component that stores data which includes at least tracking data and the interaction data; and

a filtering component that collaboratively filters the data and suggests information to the user.

2. The system of claim 1, wherein the interaction data is related to at least one of speed of the user as the user nears the location and dwell of the user at the location.

3. The system of claim 1, wherein the tracking component utilizes a geographic location technology to determine the location of the user.

4. The system of claim 1, wherein the suggested information is based on at least one of distance and disruption of a current known or inferred route.

5. The system of claim 1, wherein the information is related to a service associated with the location and a nearby location.

6. The system of claim 1, wherein the data component includes a database of information generated from a plurality of users that is continually updated and stores in association with a user at least one of dwell data, pause data, user preferences data, past data presented to the user, suggested data previously presented to the user.

7. The system of claim 1, wherein the data component stores personal data that is related to the user and which the data component anonymizes.

8. The system of claim 1, wherein the data component facilitates an abstraction process that takes location information associated with the location and abstracts to a more general category of location type.

9. The system of claim 1, further comprising a learning and reasoning component that employs a probabilistic and/or statistical-based analysis to prognose or infer an action that a user desires to be automatically performed.

10. A computer-readable medium having stored thereon computer-executable instructions for carrying out the system of claim 1.

11. A server that employs the system of claim 1.

12. A computer-implemented method of recommending information to a user, comprising:

tracking a geographic position of a user relative to a location;

sensing at least one of pause data and velocity data of the user relative to the location;

collaboratively processing stored information of other users that have visited the location, based on the dwell data; and

presenting to the user a suggestion that includes information related to at least one of another location and a service.

13. The method of claim 12, further comprising an act of clustering data of the stored information based on demographics data associated with the user and deriving the suggestion from the clustered data, or from other approaches to collaborative filtering.

14. The method of claim 12, further comprising an act of modifying type and content of the suggestion based on past history of the user frequenting the location.

15. The method of claim 12, further comprising an act of deriving the suggestion based on the location being associated with a communications shadow.

16. The method of claim 12, further comprising an act of deriving the suggestion based on a model that predicts a likelihood that a user would already be familiar with a location or service, including the use of measures of overall popularity or salience of a location or service.

17. The method of claim 12, further comprising an act of deriving the suggestion based upon filtered results of the act of processing and modifying the filtered results based on user preferences.

18. The method of claim 12, further comprising an act of deriving the suggestion based upon a likelihood that the user already knows of the location.

19. The method of claim 12, further comprising an act of deriving the suggestion based upon filtering or an inference made that is based upon month, season, time of day and/or day of week data, and such contextual data as weather, and whether the day is a holiday or a school holiday.

20. A system that facilitates recommending information to a user, comprising:

means for tracking a geographic position of a user relative to a location;

means for sensing interaction data of the user relative to the location;

means for processing stored information associated with other users;

means for deriving a suggestion based upon results of the means for processing; and

means for presenting the suggestion in combination with enticement information that suggests to the user to go elsewhere.