Recommendation agent using a personality model determined from mobile device data

Abstract

A user's context history is analyzed to build a personality model describing the user's personality and interests. The personality model includes a plurality of metrics indicating the user's position on a plurality of personality dimensions, such as desire for novelty, tendency for extravagance, willingness to travel, love of the outdoors, preference for physical activity, and desire for solitude. A customized recommendation agent is then built based on the personality model, which selects a recommendation from a corpus to present to the user based on an affinity between the user's personality and the selected recommendation.

What is claimed is: 
     
         1 . A method of creating a customized recommendation agent for a user, the method comprising:
 obtaining a plurality of labelled context slices derived from context data associated with a user, each labelled context slice including a time, a location, and a user context label specifying at least a place inferred from the location;   obtaining place features of places included in the obtained plurality of labelled context slices, the obtained place features relevant to personality traits of the user;   identifying, using the plurality of labelled context slices, one or more home areas corresponding to one or more places at which the user has spent a majority of time spanned by the labelled context slices;   identifying, from the places included in the plurality of labelled context slices, one or more non-home areas corresponding to one or more places that do not correspond to the one or more home areas;   determining a home area statistic and a non-home area statistic from the obtained place features, the home area statistic describing place features of the one or more home areas, the non-home area statistic describing place features of the one or more non-home areas;   determining, by a processor, a personality metric based on the home area statistic and the non-home area statistic, the personality metric quantifying a personality trait dimension of the user; and   creating the customized recommendation agent configured to provide a recommendation to the user responsive to the personality metric indicating the user is likely to find value in the recommendation.   
     
     
         2 . The method of  claim 1 ,
 wherein obtaining the place features comprises determining a category that groups similar places in one of the identified non-home areas, and   wherein determining the non-home area statistic comprises determining a frequency of visits to the category of the one of the non-home areas.   
     
     
         3 . The method of  claim 1 ,
 wherein obtaining the place features comprises determining distances from the identified one or more non-home areas to a geographically nearest home area, and   wherein determining the non-home area statistics comprises determining a non-home area statistic summarizing the determined distances from the identified one or more non-home areas to the geographically nearest home area.   
     
     
         4 . The method of  claim 1 , wherein determining the home area statistic comprises: determining a proportion of visits to an identified home area relative to total visits to the places specified by the plurality of labelled context slices. 
     
     
         5 . The method of  claim 1 , wherein the personality trait dimension includes at least one of: desire for novelty, desire for extravagance, willingness to travel, love of the outdoors, preference for physical activity, and desire for solitude. 
     
     
         6 . The method of  claim 1 , wherein the customized recommendation agent provides the recommendation to the user by performing steps comprising:
 identifying a reason why the recommendation was selected; and   providing the reason for presentation to the user in conjunction with the recommendation.   
     
     
         7 . The method of  claim 1 , wherein the customized recommendation agent provides the recommendation to the user by performing steps comprising:
 receiving an input context associated with the user;   selecting the recommendation from a corpus of recommendations based on the determined personality metric and the input context; and   providing the recommendation for presentation to the user.   
     
     
         8 . The method of  claim 7 , wherein selecting the recommendation comprises:
 determining a weight for each of a plurality of recommendations from the corpus, each weight based on a degree of correspondence between the personality metric and a corresponding recommendation; and   selecting the recommendation from the plurality of recommendations responsive to the weight corresponding to the recommendation.   
     
     
         9 . The method of  claim 8 , wherein the recommendation corresponds to a venue, wherein determining the weight comprises:
 adjusting the weight corresponding to the recommendation based on a rating for the venue provided by another user.   
     
     
         10 . The method of  claim 1 , further comprising:
 receiving feedback indicating how the user responded to the recommendation; and   updating the personality metric based on the feedback.   
     
     
         11 . The method of  claim 10 , wherein the feedback indicates at least one of: the user following the recommendation, the user adding the recommendations to a plan, the user partially following the recommendation, and the user rejecting the recommendation. 
     
     
         12 . The method of  claim 1 , further comprising:
 providing the user with a series of questions, each question a binary choice that determines affinity for one of the personality trait dimensions; and   adjusting at least one personality metric of the plurality based on the user's responses to the series of questions.   
     
     
         13 . A non-transitory computer-readable storage medium comprising executable computer program code, the computer program code comprising instructions for:
 obtaining a plurality of labelled context slices derived from context data associated with a user, each labelled context slice including a time, a location, and a user context label specifying at least a place inferred from the location;   obtaining place features of places included in the obtained plurality of labelled context slices, the obtained place features relevant to personality traits of the user;   identifying, using the plurality of labelled context slices, one or more home areas corresponding to one or more places at which the user has spent a majority of time spanned by the labelled context slices;   identifying, from the places included in the plurality of labelled context slices, one or more non-home areas corresponding to one or more places that do not correspond to the one or more home areas;   determining a home area statistic and a non-home area statistic from the obtained place features, the home area statistic describing place features of the one or more home areas, the non-home area statistic describing place features of the one or more non-home areas;   determining a personality metric based on the home area statistic and the non-home area statistic, the personality metric quantifying a personality trait dimension of the user; and   creating the customized recommendation agent configured to provide a recommendation to the user responsive to the personality metric indicating the user is likely to find value in the recommendation.   
     
     
         14 . The medium of  claim 13 ,
 wherein obtaining the place features comprises determining a category that groups similar places in one of the identified non-home areas, and   wherein determining the non-home area statistic comprises determining a frequency of visits to the category of the one of the non-home areas.   
     
     
         15 . The medium of  claim 13 ,
 wherein obtaining the place features comprises determining distances from the identified one or more non-home areas to a geographically nearest home area, and   wherein determining the non-home area statistics comprises determining a non-home area statistic summarizing the determined distances from the identified one or more non-home areas to the geographically nearest home area.   
     
     
         16 . The medium of  claim 13 , wherein determining the home area statistic comprises: determining a proportion of visits to an identified home area relative to total visits to the places specified by the plurality of labelled context slices. 
     
     
         17 . The medium of  claim 13 , wherein the customized recommendation agent is configured to provide the recommendation to the user by performing steps comprising:
 receiving an input context associated with the user;   selecting the recommendation from a corpus of recommendations based on the determined personality metric and the input context; and   providing the recommendation for presentation to the user.   
     
     
         18 . The medium of  claim 17 , wherein selecting the recommendation comprises:
 determining a weight for each of a plurality of recommendations from the corpus, each weight based on a degree of correspondence between the personality metric and a corresponding recommendation; and   selecting the recommendation from the plurality of recommendations responsive to the weight corresponding to the recommendation.   
     
     
         19 . The medium of  claim 13 , wherein the computer program code further comprises instructions for:
 receiving feedback indicating how the user responded to the recommendation; and   updating the personality metric based on the feedback.   
     
     
         20 . A system for creating a customized recommendation agent for a user, the system comprising:
 a processor; and   a non-transitory computer-readable storage medium comprising computer program code executable by the processor, the computer program code comprising instructions for:
 obtaining a plurality of labelled context slices derived from context data associated with a user, each labelled context slice including a time, a location, and a user context label specifying at least a place inferred from the location; 
 obtaining place features of places included in the obtained plurality of labelled context slices, the obtained place features relevant to personality traits of the user; 
 identifying, using the plurality of labelled context slices, one or more home areas corresponding to one or more places at which the user has spent a majority of time spanned by the labelled context slices; 
 identifying, from the places included in the plurality of labelled context slices, one or more non-home areas corresponding to one or more places that do not correspond to the one or more home areas; 
 determining a home area statistic and a non-home area statistic from the obtained place features, the home area statistic describing place features of the one or more home areas, the non-home area statistic describing place features of the one or more non-home areas; 
 determining a personality metric based on the home area statistic and the non-home area statistic, the personality metric quantifying a personality trait dimension of the user; and 
 creating the customized recommendation agent configured to provide a recommendation to the user responsive to the personality metric indicating the user is likely to find value in the recommendation.