Personalized vehicle matching based upon user preferences

Abstract

Systems and methods for guided vehicle matching are disclosed. In order to generate vehicle recommendations for a user of an electronic vehicle listing service, a plurality of vehicle-related lifestyle options are presented to the user, and a user selection received. Additional data regarding the user's vehicle preferences, requirements, or usage may be obtained. Based upon such information, a set of example vehicles is generated and presented to the user. Each example vehicle has characteristics representing a plurality of other vehicles. Based upon user ratings or selections of at least some of the example vehicles, a plurality of vehicle recommendations for specific available vehicles are generated and presented to the user. In some embodiments, further user interaction with such recommendations is used to refine the vehicle recommendations and identify additional vehicle recommendations.

What is claimed is: 
     
         1 . A computer-implemented method for guided vehicle matching, comprising:
 obtaining, by one or more processors of a server, initial user preference data regarding vehicles;   generating, by the one or more processors of the server, a set of example vehicles based upon the initial user preference data, wherein each example vehicle in the set of example vehicles is a specific vehicle from an electronic vehicle listing service and wherein each respective specific vehicle is representative of a plurality of vehicle models of a corresponding type of vehicles;   updating, by the one or more processors of the server, a user interface of a client computing device to present the set of example vehicles as representatives of the corresponding pluralities of types of vehicles to the user at a display of the client computing device, wherein a user-selectable feedback option indicating a user response is presented for each of the example vehicles;   receiving, at the one or more processors of the servers, a set of user preference data including a plurality of user responses indicating affirmative user preferences for a respective plurality of example vehicles of the set of example vehicles via a corresponding plurality of user-selectable feedback option displayed at the client computing device;   determining, by the one or more processors of the server, a plurality of preferred vehicle characteristics based upon the user preference data and characteristics of the plurality of the example vehicles;   applying, by the one or more processors of the server, a trained machine learning model to the plurality of preferred vehicle characteristics and the initial user preference data to determine a plurality of refined vehicle recommendations associated with available vehicles from the electronic vehicle listing service matching at least some of the preferred vehicle characteristics; and   updating, by the one or more processors of the server, the user interface of the client computing device to present the plurality of refined vehicle recommendations to the user at the client computing device.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein obtaining the initial user preference data comprises:
 receiving, at the one or more processors of the server, a user request to generate a plurality of vehicle recommendations from a client computing device associated with a user;   causing, by the one or more processors of the server, a plurality of lifestyle options associated with vehicle usage to be presented to the user at the client computing device, wherein the plurality of lifestyle options include a plurality of categories relating to types of vehicle usage and subcategories relating to details associated with the types of vehicle usage;   receiving, at the one or more processors of the server, a user lifestyle selection indicating one of the plurality of lifestyle options; and   obtaining, by the one or more processors of the server, additional vehicle usage data associated with vehicle preferences of the user,   wherein the initial user preference data comprises the user lifestyle selection and the additional vehicle usage data.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein generating the set of example vehicles based upon the initial user preference data comprises applying the trained machine learning model to the initial user preference data to determine the set of example vehicles from the electronic vehicle listing service for presentation to the user. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein obtaining the initial user preference data comprises obtaining additional vehicle usage data by:
 causing, by the one or more processors of the server, one or more additional vehicle usage options to be presented to the user at the client computing device; and   receiving, by the one or more processors of the server, one or more user selections associated with the one or more additional vehicle usage options indicating the additional vehicle usage data.   
     
     
         5 . The computer-implemented method of  claim 1 , wherein the set of example vehicles comprises a plurality of specific vehicle models having different body types or styles. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein determining the plurality of preferred vehicle characteristics comprises:
 identifying, by the one or more processors of the server, characteristics of the plurality of the example vehicles; and   generating, by the one or more processors of the server, a vehicle preferences profile for the user based upon the characteristics of the plurality of the example vehicles,   wherein the plurality of refined vehicle recommendations are determined using the vehicle preference profile.   
     
     
         7 . The computer-implemented method of  claim 1 , wherein determining the plurality of refined vehicle recommendations includes:
 identifying a plurality of vehicle listings of the electronic vehicle listing service matching at least some of the preferred vehicle characteristics;   calculating a matching score for each of the plurality of vehicle listings using the trained machine learning model; and   determining the plurality of refined vehicle recommendations based upon the matching scores.   
     
     
         8 . The computer-implemented method of  claim 1 , further comprising:
 receiving, at the one or more processors of the server, user reaction data indicating responses of the user to one or more of the plurality of refined vehicle recommendations;   adjusting, by the one or more processors of the server, the plurality of preferred vehicle characteristics based upon the user reaction data;   identifying, by the one or more processors of the server, a plurality of additional vehicle recommendations associated with additional available vehicles matching the preferred vehicle characteristics as adjusted based upon the user reaction data; and   updating, by the one or more processors of the server, the user interface of the client computing device to present the plurality of additional vehicle recommendations to the user at the client computing device.   
     
     
         9 . The computer-implemented method of  claim 8 , wherein the user reaction data includes data regarding user navigation through one or more pages associated with the one or more refined vehicle recommendations in a web site or application. 
     
     
         10 . The computer-implemented method of  claim 8 , wherein the user reaction data includes user ratings of the one or more refined vehicle recommendations. 
     
     
         11 . A computer system for guided vehicle matching, comprising:
 one or more processors;   a program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
 obtain initial user preference data regarding vehicles; 
 generate a set of example vehicles based upon the initial user preference data, wherein each example vehicle in the set of example vehicles is a specific vehicle from an electronic vehicle listing service and wherein each respective specific vehicle is representative of a plurality of vehicle models of a corresponding type of vehicles; 
 update a user interface of a client computing device to present the set of example vehicles as representatives of the corresponding pluralities of types of vehicles to the user at a display of the client computing device, wherein a user-selectable feedback option indicating a user response is presented for each of the example vehicles; 
 receive a set of user preference data including a plurality of user responses indicating affirmative user preferences for a respective plurality of example vehicles of the set of example vehicles via a corresponding plurality of user-selectable feedback option displayed at the client computing device; 
 determine a plurality of preferred vehicle characteristics based upon the user preference data and characteristics of the plurality of the example vehicles; 
 apply a trained machine learning model to the plurality of preferred vehicle characteristics and the initial user preference data to determine a plurality of refined vehicle recommendations associated with available vehicles from the electronic vehicle listing service matching at least some of the preferred vehicle characteristics; and 
 update the user interface of the client computing device to present the plurality of refined vehicle recommendations to the user at the client computing device. 
   
     
     
         12 . The computer system of  claim 11 , wherein the executable instructions that cause the computer system to obtain the initial user preference data cause the computer system to:
 receive a user request to generate a plurality of vehicle recommendations from a client computing device associated with a user;   cause a plurality of lifestyle options associated with vehicle usage to be presented to the user at the client computing device, wherein the plurality of lifestyle options include a plurality of categories relating to types of vehicle usage and subcategories relating to details associated with the types of vehicle usage;   receive a user lifestyle selection indicating one of the plurality of lifestyle options; and   obtain additional vehicle usage data associated with vehicle preferences of the user,   wherein the initial user preference data comprises the user lifestyle selection and the additional vehicle usage data.   
     
     
         13 . The computer system of  claim 11 , wherein the executable instructions that cause the computer system to generate the set of example vehicles based upon the initial user preference data cause the computer system to apply the trained machine learning model to the initial user preference data to determine the set of example vehicles from the electronic vehicle listing service for presentation to the user. 
     
     
         14 . The computer system of  claim 11 , wherein the executable instructions that cause the computer system to determine the plurality of preferred vehicle characteristics cause the computer system to:
 identify characteristics of the plurality of the example vehicles; and   generate a vehicle preferences profile for the user based upon the characteristics of the plurality of the example vehicles,   wherein the plurality of refined vehicle recommendations are determined using the vehicle preference profile.   
     
     
         15 . The computer system of  claim 11 , wherein the executable instructions further cause the computer system to:
 receive user reaction data indicating responses of the user to one or more of the plurality of refined vehicle recommendations;   adjust the plurality of preferred vehicle characteristics based upon the user reaction data;   identify a plurality of additional vehicle recommendations associated with additional available vehicles matching the preferred vehicle characteristics as adjusted based upon the user reaction data; and   update the user interface of the client computing device to present the plurality of additional vehicle recommendations to the user at the client computing device.   
     
     
         16 . A tangible, non-transitory computer-readable medium storing executable instructions for guided vehicle matching that, when executed by one or more processors of a computer system, cause the computer system to:
 obtain initial user preference data regarding vehicles;   generate a set of example vehicles based upon the initial user preference data, wherein each example vehicle in the set of example vehicles is a specific vehicle from an electronic vehicle listing service and wherein each respective specific vehicle is representative of a plurality of vehicle models of a corresponding type of vehicles;   update a user interface of a client computing device to present the set of example vehicles as representatives of the corresponding pluralities of types of vehicles to the user at a display of the client computing device, wherein a user-selectable feedback option indicating a user response is presented for each of the example vehicles;   receive a set of user preference data including a plurality of user responses indicating affirmative user preferences for a respective plurality of example vehicles of the set of example vehicles via a corresponding plurality of user-selectable feedback option displayed at the client computing device;   determine a plurality of preferred vehicle characteristics based upon the user preference data and characteristics of the plurality of the example vehicles;   apply a trained machine learning model to the plurality of preferred vehicle characteristics and the initial user preference data to determine a plurality of refined vehicle recommendations associated with available vehicles from the electronic vehicle listing service matching at least some of the preferred vehicle characteristics; and   update the user interface of the client computing device to present the plurality of refined vehicle recommendations to the user at the client computing device.   
     
     
         17 . The tangible, non-transitory computer-readable medium of  claim 16 , wherein the executable instructions that cause the computer system to obtain the initial user preference data cause the computer system to:
 receive a user request to generate a plurality of vehicle recommendations from a client computing device associated with a user;   cause a plurality of lifestyle options associated with vehicle usage to be presented to the user at the client computing device, wherein the plurality of lifestyle options include a plurality of categories relating to types of vehicle usage and subcategories relating to details associated with the types of vehicle usage;   receive a user lifestyle selection indicating one of the plurality of lifestyle options; and   obtain additional vehicle usage data associated with vehicle preferences of the user,   wherein the initial user preference data comprises the user lifestyle selection and the additional vehicle usage data.   
     
     
         18 . The tangible, non-transitory computer-readable medium of  claim 16 , wherein the executable instructions that cause the computer system to generate the set of example vehicles based upon the initial user preference data cause the computer system to apply the trained machine learning model to the initial user preference data to determine the set of example vehicles from the electronic vehicle listing service for presentation to the user. 
     
     
         19 . The tangible, non-transitory computer-readable medium of  claim 16 , wherein the executable instructions that cause the computer system to determine the plurality of preferred vehicle characteristics cause the computer system to:
 identify characteristics of the plurality of the example vehicles; and   generate a vehicle preferences profile for the user based upon the characteristics of the plurality of the example vehicles,   wherein the plurality of refined vehicle recommendations are determined using the vehicle preference profile.   
     
     
         20 . The tangible, non-transitory computer-readable medium of  claim 16 , wherein the executable instructions further cause the computer system to:
 receive user reaction data indicating responses of the user to one or more of the plurality of refined vehicle recommendations;   adjust the plurality of preferred vehicle characteristics based upon the user reaction data;   identify a plurality of additional vehicle recommendations associated with additional available vehicles matching the preferred vehicle characteristics as adjusted based upon the user reaction data; and   update the user interface of the client computing device to present the plurality of additional vehicle recommendations to the user at the client computing device.