Method and apparatus for generating recommendations based on user preferences and environmental characteristics

• US 7,412,202 B2
• Filed: 04/03/2001
• Issued: 08/12/2008
• Est. Priority Date: 04/03/2001
• Status: Expired due to Term

• Current Assignee: Sisvel S.p.A. (Fineur International S.A.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Lee, Mi-Suen, Agnihotri, Lalitha, Gutta, Srinivas
• Primary Examiner(s): Nguyen, Duc M
• Application Number:
  US09/825,328
• Publication Number:
  US 20020142722A1
• Time in Patent Office: 2,688 Days
• Field of Search: 455/414.1, 455/414.3, 455 301- 304, 455/432.3, 455/456.1, 455/456.5, 705/10, 705/26, 725/45, 725/46
• US Class Current: 455/3.01
• CPC Class Codes: H04H 60/40 for identifying broadcast time
• CPC Class Codes: H04H 60/51 of receiving stations
• CPC Class Codes: H04H 60/65 for using the result on users' side
• CPC Class Codes: H04N 21/42202 environmental sensors, e.g. for detecting temperature, luminosity, pressure, earthquakes
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/4524 involving the geographical location of the client retrieval from the Internet by querying based on geographical locations G06F16/9537; systems specially adapted for using geographical information in broadcast systems H04H60/70; protocols in which the network application is adapted for the location of the user terminal in communication control or processing H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/454 Content or additional data filtering, e.g. blocking advertisements
• CPC Class Codes: H04N 21/4665 involving classification methods, e.g. Decision trees
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/8106 involving special audio data, e.g. different tracks for different languages
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

A recommendation system is disclosed that generates recommendations for one or more items based on user preferences and one or more environmental factors. The user'"'"'s preferences are learned under various environmental conditions using an environmental data collection system. The observed environmental conditions may include, for example, location, characteristics of the location, weather or characteristics of the user'"'"'s motion, such as a rate of movement. For each positive and negative behavioral example, a number of attributes of the selected item are classified in the user profile together with the prevailing environmental conditions. When recommending an item, the disclosed recommender retrieves the user preferences and evaluates the current environmental conditions. A recommendation score can be generated for each available item based on the user'"'"'s demonstrated preferences under similar environmental conditions, such as in the same or a similar geographic area or under similar weather conditions.

Claims

1. A method for recommending an item to a user, comprising the steps of:

observing one or more environmental characteristics;

learning preferences of said user for each item to be recommended while exposed to and under said one or more observed environmental characteristics; and

generating a recommendation score for said item based on features of said item and said learned preferences of said user under said one or more observed environmental characteristics, wherein said one or more observed environmental characteristics includes at least one of a weather condition, a characteristic of motion of said user, a location, and one or more characteristics of said location and each of the one or more observed environmental characteristics is associated with a weight assigned by the user.

2. The method of claim 1, wherein said item is a radio station.

3. The method of claim 1, wherein said item is content.

4. The method of claim 1, wherein said item is a product.

5. The method of claim 1, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile.

6. The method of claim 5, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender.

7. A method for recommending an item to a user, comprising the steps of:

observing one or more environmental characteristics for obtaining information characterizing said one or more environmental characteristics for a given time;

learning preferences of said user for each item to be recommended while said user is exposed to and affected by said one or more observed environmental characteristics; and

generating a recommendation score for said item based on features of said item, and said learned preferences of said user under said one or more observed environmental characteristics for a given time, wherein said one or more observed environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user.

8. The method of claim 7, wherein said item is a radio station.

9. The method of claim 7, wherein said item is content.

10. The method of claim 7, wherein said item is a product.

11. The method of claim 7, wherein said one or more observed environmental characteristics is a user location at said given time.

12. The method of claim 7, wherein said one or more observed environmental characteristics is one or more characteristics of a location.

13. The method of claim 7, wherein said observed preferences of said user under said one or more environmental conditions are recorded in a profile.

14. The method of claim 13, wherein said observed preferences of said user under said one or more environmental conditions are recorded in a profile as a set of rules by a decision tree recommender.

15. A system for recommending an item to a user, comprising:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

observe one or more environmental characteristics for obtaining information characterizing said one or more environmental characteristics for a given time

learn preferences of said user for each item to be recommended while exposed to and under said one or more observed environmental characteristics; and

generate a recommendation score for said item based on features of said item and said learned preferences of said user under said one or more observed environmental characteristics, wherein said one or more observed environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user.

16. The system of claim 15, wherein said item is a radio station.

17. The system of claim 15, wherein said item is content.

18. The system of claim 15, wherein said item is a product.

19. The system of claim 15, wherein said one or more observed environmental characteristics is a location.

20. The system of claim 15, wherein said one or more observed environmental characteristics is one or more characteristics of location.

21. The system of claim 15, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile.

22. The system of claim 21, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender.

23. A system for recommending an item to a user, comprising:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

observe at least one motion characteristic for obtaining information characterizing said one or more environmental characteristics for a given time;

learn preferences of said user for each item to be recommended while exposed to and under said characteristic of motion;

obtain information characterizing said characteristic of motion of said user for a given time; and

generate a recommendation score for said item based on features of said item, and said learned preferences of said user under said characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user.

24. The system of claim 23, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile.

25. The system of claim 24, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender.

26. A computer readable medium having computer readable code means embodied thereon for recommending an item to a user, said computer readable program code means comprising:

a step to learn preferences of said user for each item to be recommended while exposed to and under one or more observed environmental characteristics; and

a step to generate a recommendation score for said item based on features of said item and said learned preferences of said user under said one or more observed environmental characteristics, wherein said one or more environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user.

27. The article of manufacture of claim 26, wherein said item includes at least one of a radio station, content and product.

28. The article of manufacture of claim 26, wherein said one or more observed environmental characteristics further includes at least one of a location, and one or more characteristics of said location.

29. The article of manufacture of claim 26, wherein said learneded preferences of said user under said one or more environmental characteristics are recorded in a profile.

30. The article of manufacture of claim 26, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender.

31. A computer readable medium having computer readable code means embodied thereon for recommending an item to a user, said computer readable program code means comprising:

a step to obtain information characterizing one or more observed environmental characteristics for a given time;

a step to learn preferences of said user for each item to be recommended by observing said user'"'"'s personal exposure to one or more observed environmental characteristics; and

a step to generate a recommendation score for said item based on features of said item, and said learned preferences of said user under said one or more observed environmental characteristics for a given time, wherein said one or more observed environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user.

32. The article of manufacture of claim 31, wherein said item includes at least one of a radio station, content and product.

33. The article of manufacture of claim 31, wherein said one or more observed environmental characteristics further includes at least one of a location, and one or more characteristics of said location.

34. The article of manufacture of claim 31, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile.

35. The article of manufacture of claim 31, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender.