WO2026023671A1 - Automobile recommendation system - Google Patents

Abstract

An automobile recommendation system (1) recommends an automobile to a user and is provided with: a memory for storing an instruction; and one or more processors. When the instruction is executed, the one or more processors cause the automobile recommendation system (1) to: determine first-group users belonging to an attribute that is the same as or similar to an attribute of a specific user at a prescribed time point; calculate an indicator that represents preference of the user from the difference between a characteristic value of a vehicle type requested by the first-group users and a characteristic value of a vehicle type requested by the specific user at the prescribed time point; determine second-group users belonging to an attribute that is the same as or similar to the current attribute of the specific user; calculate an added characteristic value of the vehicle type on the basis of a characteristic value of a vehicle type requested by the second-group users and the indicator that represents the preference of the specific user; determine a candidate vehicle type on the basis of the added characteristic value of the vehicle type and a characteristic value of each vehicle type; and recommend the candidate vehicle type to the specific user.

Description

Car recommendation system

The present invention relates to technology for recommending products to individual users, and in particular to technology that is effective when applied to automobile recommendation systems that recommend automobiles.

When selling products, systems are being considered that recommend appropriate products based on the user's needs and values. However, user needs and values cannot usually be observed directly unless the user is asked about them through a survey or other means.

In response to this, for example, Japanese Patent Application Laid-Open No. 2022-90717 (Patent Document 1) describes a method of estimating the characteristics (needs) desired by a user for specific characteristic items of a product (e.g., an automobile) based on the user's attribute information, and then comparing the estimated needs with the actual characteristics of each product to selectively suggest products that meet the user's needs.

Japanese Patent Application Laid-Open No. 2022-90717

Using conventional technology, it is possible to estimate a user's needs based on their attributes and recommend products that meet those needs to the user.

In this case, to estimate needs (tendencies) from user attributes, for example, groups of users with the same or similar attributes are segmented and the individual needs of these users are averaged. However, it is not necessarily the case that the same products should be recommended to users with the same or similar attributes. Even if the attributes are the same, individual users have different values (tastes), so it is necessary to recommend products that match those values. However, such values cannot usually be observed directly unless the user is asked.

The object of the present invention is to provide an automobile recommendation system that recommends products (especially automobiles) by inferring differences and changes in needs due to differences in user attributes and the values of individual users.

The above and other objects and novel features of the present invention will become apparent from the description of this specification and the accompanying drawings.

Among the inventions disclosed in this application, the most representative ones can be briefly outlined as follows:

A representative embodiment of the present invention is an automobile recommendation system that includes a memory for storing instructions and one or more processors. When the instructions are executed, the one or more processors cause the automobile recommendation system to: determine a first group of users whose attributes are the same as or similar to the attributes of a specific user at a given time; calculate an index indicating the user's values from the difference between the characteristic values of the vehicle models requested by the users of the first group and the characteristic values of the vehicle models requested by the specific user at the given time; determine a second group of users whose attributes are the same as or similar to the specific user's current attributes; calculate an aggregate characteristic value of the vehicle models based on the characteristic values of the vehicle models requested by the users of the second group and the index indicating the specific user's values; determine candidate vehicle models based on the aggregate characteristic value of the vehicle models and the characteristic values of each vehicle model; and recommend the candidate vehicle models to the specific user.

The effects achieved by representative inventions disclosed in this application can be briefly explained as follows:

In other words, according to a representative embodiment of the present invention, it is possible to recommend cars by inferring differences and changes in needs due to differences in user attributes, as well as the values of individual users.

1 is a diagram illustrating an overview of a configuration example of an automobile recommendation system according to an embodiment of the present invention. 1A and 1B are diagrams outlining an example of a concept for recommending a car by inferring the needs and values of a user according to an embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of vehicle-specific performance specifications according to an embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of a characteristic value for each vehicle type according to an embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of a vehicle model owned by a user according to an embodiment of the present invention. FIG. 10 is a diagram showing an outline of an example of a data configuration of user attributes at the time of possession start in one embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of vehicle model characteristics requested by each user in one embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of requested vehicle model characteristics for each user attribute in one embodiment of the present invention. FIG. 1 is a diagram showing an outline of an example of a data configuration of values for each user in one embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of current user characteristics in one embodiment of the present invention. FIG. 2 is a diagram showing an outline of an example of a data configuration of current user-requested vehicle model characteristics in one embodiment of the present invention. FIG. 2 is a diagram illustrating an example of a data configuration of recommended vehicle models according to an embodiment of the present invention. FIG. 1 is a diagram illustrating an example of a hardware configuration according to an embodiment of the present invention. 1 is a flowchart showing a series of operations of a recommendation process executed in an automobile recommendation system according to an embodiment of the present invention.

Embodiments of the present invention will be described in detail below with reference to the drawings. In all drawings used to explain the embodiments, identical parts will generally be given the same reference numerals, and repeated explanations will be omitted. However, parts that have been described using reference numerals in one drawing may be referred to using the same reference numerals when explaining other drawings, although they will not be shown again.

<Overview>
As mentioned above, it is usually not possible to directly observe the needs of different users or the values of individual users. This is because it is necessary to define what "needs" and individual "values" are, and even if they can be defined, it is usually not possible to directly observe them without asking the users directly.

In contrast, when recommending a car to purchase to a user, the car recommendation system, which is one embodiment of the present invention, considers that the user's needs and values are reflected in the performance indicators of the car model currently owned by that user. In other words, since it is assumed that users will own a car that matches their own values, it is believed that it is possible to discover the user's needs and values based on the performance indicators of the car model currently owned.

Furthermore, needs based on differences in user attributes can be determined as representative values (such as averages or medians) of vehicle performance indicators required by a group of users with the same or similar attributes (hereinafter sometimes referred to simply as "same attributes"), and the values of individual users can be evaluated as the difference between the performance indicators they require and these representative values (for example, "this user places more importance on fuel efficiency than the average user in their 30s"). These user values are thought to stem from something the user is innately endowed with, and will not change significantly even if their life stage changes.

Figure 2 is a diagram outlining an example of the concept of recommending cars by inferring a user's needs and values in one embodiment of the present invention. Figure 2(a) shows an example of inferring a user's values. The horizontal axis of the graph shows car characteristic items (four in the example of Figure 2, "Characteristic 1" to "Characteristic 4"), and the vertical axis shows the strength of each characteristic item. For each characteristic item, the open graph shows the vehicle model characteristics (representative values) desired by a group of users with the same attributes as the target user at the time the target user began owning the car they currently own (at the time of ownership start). The shaded graph shows the vehicle model characteristics desired by the individual user at the time of ownership start.

In this embodiment, the difference between the open graph and the shaded graph for each characteristic item in this graph, i.e., the difference between the representative value (tendency) of a group of users with the same attribute and the characteristic value desired by the individual user, is considered to represent the user's values. In the example of Figure 2(a), there is a difference between "Characteristic 1" and "Characteristic 3" (the black arrow in the figure), and these represent the user's values. Note that the reason the time when the user first owned the car they currently own is used as the basis here is because at the time of ownership, there are opportunities to obtain various information about the user's attributes through sales activities and various procedures, etc., and it is not necessary to use the time when ownership began as the basis, as other points in time can also be used.

Figure 2(b) shows an example of determining the characteristics of a vehicle model to be recommended to a current user based on the user's values obtained in Figure 2(a). The open graph shows the vehicle model characteristics (representative values) desired by a group of users with the same attributes as the current user. The strength of each characteristic item differs from the open graph in Figure 2(a), but this reflects differences and changes in attributes such as age (for example, needs change as people move from their 20s to their 30s due to changes in life stages, etc.).

In this embodiment, by reflecting the user's values (black arrows) determined in Figure 2(a) in this open graph (vehicle characteristics desired by a group of users with the same attributes), it is possible to determine the characteristics of recommended vehicles (shaded graph) that reflect the user's values. Then, vehicles that actually have characteristics close to (similar to) the characteristics of the recommended vehicle obtained here become the vehicles to be recommended.

In this way, by separating the tendencies of each user's attributes from the values of each individual user, it becomes possible to recommend cars according to changes in life stages. For example, it becomes possible to make recommendations that can be interpreted as follows: "As you have changed generation, your next car should be a car like XX, in line with general user tendencies. However, since this user places importance on environmental performance, we will recommend a car with relatively good fuel efficiency." Or, "If a user who drove XX when they lived in Tokyo moves to Hokkaido, we will recommend △△." Or, "If a user who lived alone and drove XX gets married and starts living with their spouse, we will recommend △△." Or, "For a user who enjoys camping, we will recommend a car model that prioritizes spatiality more than a group of users with the same attributes."

<System configuration and processing flow>
1 is a diagram showing an overview of an example configuration of a car recommendation system according to one embodiment of the present invention. The car recommendation system 1 is an information processing system that is configured, for example, by server equipment, a virtual server constructed on a cloud computing service, or an information processing terminal, and that realizes various functions related to car recommendation by executing middleware such as an OS (Operating System), a DBMS (Database Management System), and a Web server program, which are deployed on memory from a recording device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and software running on the OS (Operating System), which are deployed on memory from a recording device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and software running on the OS (Operating System), which is deployed on memory from a recording device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and software running on the OS (Operating System), which is deployed on memory from a recording device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and software running on the OS (Operating System), which is running on the OS (Operating System), and software running on the DBMS (Operating System), which are run ... OS (Operating System), which are deployed on memory from a recording device such as an HDD (Hard Disk Drive) or an SSD (Operating System), and

This automobile recommendation system 1 has various components implemented as software, such as a vehicle model characteristic calculation unit 11, a user-requested vehicle model characteristic calculation unit 12, a vehicle model characteristic calculation unit for each user attribute 13, a user-value calculation unit 14, a current user-requested vehicle model characteristic calculation unit 15, and a recommended vehicle model calculation unit 16. It also has various data stores, such as performance specifications for each vehicle model 21, vehicle models owned by the user 22, user attributes at the time of ownership start 23, current user attributes 24, characteristic values for each vehicle model 31, vehicle model characteristics requested by each user 32, vehicle model characteristics requested by each user attribute 33, user-value calculation unit 34, current user-requested vehicle model characteristics 35, and recommended vehicle models 36, which are configured as databases, files, etc.

When recommending cars to users, the vehicle model characteristic calculation unit 11 first calculates the characteristic values of each vehicle model from each manufacturer in advance. Here, the performance specification data for each vehicle model stored in the vehicle model performance specifications 21 is used as input, and characteristic values for each vehicle model are calculated based on a predetermined model obtained by machine learning, and output as vehicle model characteristic values 31. There are no particular limitations on the tools and libraries used for machine learning, and any available tools and libraries can be used as appropriate.

Figure 3 is a diagram outlining an example of the data configuration of vehicle-specific performance specifications 21 in one embodiment of the present invention. Vehicle-specific performance specifications 21 is a table that holds data on performance indexes and performance specifications for each vehicle model of each manufacturer, and includes, for example, key items such as manufacturer, body shape, vehicle model name, and model year (items marked with an asterisk in the diagram; the same applies below), as well as items related to performance specifications such as new vehicle price, horsepower, fuel efficiency, overall length, overall width, and overall height. Note that performance specification items are not limited to these, and other items may also be included. Data for each of these items can be collected automatically or manually from various media, such as each automobile manufacturer's website or catalog, commercially available data, etc.

Figure 4 is a diagram outlining an example of the data configuration of vehicle-specific characteristic values 31 in one embodiment of the present invention. Vehicle-specific characteristic values 31 are a table that holds characteristic values calculated for each vehicle model by vehicle-specific characteristic calculation unit 11 based on data from vehicle-specific performance specifications 21, and include, for example, key items such as manufacturer, body shape, vehicle model name, and model year, as well as characteristic value items such as characteristic 1, characteristic 2, characteristic 3, etc. In this embodiment, values for each of the characteristic 1, characteristic 2, characteristic 3, etc. items are calculated based on a predetermined model obtained by machine learning, so the meaning of each item and value is a black box and cannot generally be interpreted by humans. There is no particular limit on the number of items, and it is determined according to the model design.

Returning to Figure 1, next, the data on each user's currently owned vehicle model and the user's attribute information at the time of ownership start, stored in user-owned vehicle model 22 and user attributes at the time of ownership start 23, are input, and the user-requested vehicle model characteristic calculation unit 12 references the above-mentioned vehicle model characteristic value 31 to calculate what characteristics each user's owned vehicle model has, i.e., what vehicle model characteristics each user is requesting, and outputs this as user-requested vehicle model characteristics 32.

Figure 5 is a diagram outlining an example of the data structure of user-owned vehicle models 22 in one embodiment of the present invention. User-owned vehicle models 22 is a table that holds information about vehicle models owned by each user, and has, for example, a key item for the user ID, and items that identify the vehicle model, such as the manufacturer of the vehicle, the body shape of the vehicle, the name of the vehicle, and the model year of the vehicle.

FIG. 6 is a diagram outlining an example of the data configuration of user attributes 23 at the time of ownership start in one embodiment of the present invention. User attributes 23 at the time of ownership start is a table that holds information about the attributes of each user at the time that the user started owning the vehicle model, and has, for example, a key item for user ID and items related to user attributes such as gender, prefecture at the time of ownership start, and age category at the time of ownership start. Items related to user attributes are not limited to these, and may also include other attribute items such as occupation, family composition, and hobbies.

FIG. 7 is a diagram outlining an example of the data configuration of user-requested vehicle model characteristics 32 in one embodiment of the present invention. User-requested vehicle model characteristics 32 are a table that holds information on each user's attributes and the characteristics of the vehicle models they own, i.e., information on what characteristics each user required of that vehicle model at the time they began owning that vehicle model. For example, it has a key field for user ID, fields related to user attributes such as gender, prefecture at time of ownership start, and age category at time of ownership start, fields specifying the vehicle model such as the vehicle manufacturer, vehicle body shape, vehicle name, and vehicle year, and fields for characteristic values such as characteristic 1, characteristic 2, characteristic 3, etc. Each of the above fields has the same content as the corresponding fields in the input user-owned vehicle model 22, user attributes at time of ownership start 23, and vehicle-specific characteristic values 31.

Returning to Figure 1, next, the user-specific requested vehicle model characteristics 32 are input, and the user attribute-specific requested vehicle model characteristics calculation unit 13 performs aggregation and statistical processing on a user attribute basis, outputting the results as user attribute-specific requested vehicle model characteristics 33.

Figure 8 is a diagram outlining an example of the data configuration of the required vehicle model characteristics by user attribute 33 in one embodiment of the present invention. The required vehicle model characteristics by user attribute 33 is a table that holds data on the characteristics of the vehicle models owned by each user, aggregated and statistically processed by user attribute unit at the time of ownership start, i.e., information related to needs (tendencies) due to differences in user attributes, and has key items such as gender, prefecture at the time of ownership start, and age category at the time of ownership start, as well as items for characteristic values such as characteristic 1, characteristic 2, characteristic 3, etc.

In this embodiment, the user attributes to be aggregated are gender, prefecture, and age category (for example, "20s," "30s," "40s," etc.), but are not limited to this. The user's residential area may be divided into smaller areas rather than prefectures, or conversely, into larger areas such as Kanto, Tohoku, Chubu, etc. Other attribute items such as occupation and family structure may also be used. The values of each item, characteristic 1, characteristic 2, characteristic 3, etc., may be aggregated by user attribute and used as the average value, or other statistical indicators such as the median may be used.

Returning to Figure 1, next, the user-specific desired vehicle model characteristics 32 and user-specific desired vehicle model characteristics 33 are input, and the user-specific value calculation unit 14 calculates each user's value (more accurately, an "index indicating values," but simply referred to as "values") and outputs it as user-specific value 34.

FIG. 9 is a diagram outlining an example of the data configuration of per-user values 34 in one embodiment of the present invention. Per-user values 34 is a table that holds information on the differences between the representative values of vehicle model characteristics desired by a group of users with the same attributes as the user in question and the vehicle model characteristics requested by the user in question. For example, it has a key item of user ID and items for characteristic values such as characteristic 1, characteristic 2, characteristic 3, etc. Here, the values of each item of characteristic 1, characteristic 2, characteristic 3, etc. are the differences between the values of each item of characteristic 1, characteristic 2, characteristic 3, etc. in the above-mentioned user attribute required vehicle model characteristics 33 (needs (tendencies) based on the attributes of the user at the start of ownership) and the values of each item of characteristic 1, characteristic 2, characteristic 3, etc. in per-user required vehicle model characteristics 32 (vehicle model characteristics requested by the user at the start of ownership), as shown in FIG. 2(a).

Returning to Figure 1, next, the above-mentioned per-user values 34 and current user attribute 24 data are input, and by referencing the above-mentioned per-user attribute requested vehicle model characteristics 33, the current user requested vehicle model characteristics calculation unit 15 calculates information on the vehicle model characteristics requested by each current user, i.e., information on the vehicle model characteristics to be recommended to the current user, and outputs this as current user requested vehicle model characteristics 35.

Figure 10 is a diagram outlining an example of the data structure of current user characteristics 24 in one embodiment of the present invention. Current user characteristics 24 is a table that holds current attribute information for each user, and has, for example, a key item for user ID, and items related to user attributes such as gender, current age, and current prefecture. As mentioned above, the attribute items are not limited to those shown in the example of Figure 10; other attribute items such as occupation and family composition may be used, and residential areas may be identified using divisions other than prefectures.

FIG. 11 is a diagram outlining an example of the data configuration of current user-requested vehicle model characteristics 35 in one embodiment of the present invention. Current user-requested vehicle model characteristics 35 is a table that holds information on each user's current attributes and the characteristics of the vehicle model they request, i.e., information on the vehicle model characteristics each user is currently requesting. For example, it has a key item for user ID, items related to user attributes such as gender, current age, and current prefecture, and items for characteristic values such as characteristic 1, characteristic 2, characteristic 3, etc.

As shown in Figure 2(b), the values of each item of characteristic 1, characteristic 2, characteristic 3, ... are calculated by adding the values of each item of characteristic 1, characteristic 2, characteristic 3, ... in the user-specific values 34 of the target user (the values of the user) to the values of each item of characteristic 1, characteristic 2, characteristic 3, ... obtained from user attribute required vehicle characteristics 33 based on the target user's current attribute information (needs (tendencies) based on the user's current attributes). In other words, the values of each item of characteristic 1, characteristic 2, characteristic 3, ... here indicate the characteristics of the vehicle model that should be recommended to the user.

Returning to Figure 1, next, the current user-requested vehicle characteristics 35 are input, and the recommended vehicle model calculation unit 16 calculates specific vehicle model candidates to recommend by referring to the vehicle-specific characteristic values 31, and then references the vehicle-specific performance specifications 21 to output the specific recommended vehicle model 36. Here, one or more vehicle models having characteristic values similar to the values of each item of characteristic 1, characteristic 2, characteristic 3, etc. in the current user-requested vehicle model characteristics 35 are extracted from the vehicle-specific characteristic values 31 and used as candidates for the specific vehicle model to recommend. Note that there are no particular limitations on the method for determining similarity, and any appropriate method can be used, such as vectorizing the values of each item of characteristic 1, characteristic 2, characteristic 3, etc. as elements and evaluating the similarity of the vectors.

Figure 12 is a diagram outlining an example of the data structure of recommended vehicle models 36 in one embodiment of the present invention. Recommended vehicle models 36 is a table that holds information about vehicle models and their characteristics recommended for each user, and includes key items such as user ID, manufacturer, body shape, vehicle model name, and model year, as well as performance specifications such as new vehicle price, horsepower, fuel economy, overall length, overall width, and overall height.

Note that the data structure examples shown in Figures 3 to 12 show logical table structures (views), and in reality, for example, multiple logical tables in a database may be implemented in a normalized form as one or more physical tables.

Furthermore, in this embodiment, the example is one in which each user currently owns a car, i.e., a case in which the user is replacing their car, but this is not limited to this. As long as it is possible to obtain information that can ascertain the characteristics of the car model requested by the user, it is not necessary for the user to currently own a car, and the system can also be applied to cases in which a new car is being purchased. For example, if it is determined from the user's web browsing history that they are intensively browsing a specific car model, the characteristics of that car model may be treated as the characteristics of the car model requested by the user.

<An example of the hardware configuration of a car recommendation system>
13 , an example of the hardware configuration of, for example, a server device included in the automobile recommendation system 1 will be described. The server device 1300 includes a memory 1301, a processor 1302, a communication interface 1303, a storage 1304, an input interface 1305, a bus 1306, and the like.

Memory 1301 is a volatile memory such as DRAM (Dynamic Random Access Memory) that temporarily stores programs and the results of calculations by a processor such as a CPU. Processor 1302 includes one or more processors such as a CPU. Communication interface 1303 includes an interface for wired or wireless communication, and communicates with, for example, an information processing terminal via the Internet or a specific network. Communication interface 1303 includes a communication circuit for realizing communication with, for example, an information processing terminal. Storage 1304 includes a recording device such as an HDD or SSD, and stores, for example, middleware such as an OS, DBMS, or web server program, or software for recommending automobiles. Input interface 1305 includes a keyboard that accepts operations by an administrator managing the server equipment, but this may not be necessary if the server equipment is operated remotely via a network. Bus 1306 transmits data between blocks of the server equipment.

By the processor 1302 expanding and executing the computer program stored in the storage 1304 in the memory 1301, the functions of each unit, such as the vehicle model characteristics calculation unit 11, the user-requested vehicle model characteristics calculation unit 12, the user attribute-requested vehicle model characteristics calculation unit 13, the user-requested value calculation unit 14, the current user-requested vehicle model characteristics calculation unit 15, and the recommended vehicle model calculation unit 16, which were described above with reference to FIG. 1, are realized.

<A series of operations for processing car recommendations>
A series of operations for processing a car recommendation (also simply referred to as recommendation processing) executed in the car recommendation system will be described with reference to Fig. 14. The recommendation processing is realized, for example, by the processor 1302 of the server device 1300 loading a computer program stored in the storage 1304 into the memory 1301 and executing it.

Before the recommendation process shown in FIG. 14 begins, the processor 1302 calculates characteristic values for each vehicle model by applying a machine learning model to the performance specification data for each vehicle model stored in the vehicle-specific performance specifications 21. The processor 1302 stores the calculated characteristic values for each vehicle model (as vehicle-specific characteristic values 31) in the storage 1304. The process of calculating the characteristic values for each vehicle model corresponds to the process performed by the vehicle model characteristic calculation unit 11 described above.

The characteristic values of each vehicle model are data in which the performance specifications of each vehicle model have been reduced to a feature vector of a predetermined dimension (a vector expressed in a lower dimension than the performance specification data, but with enough dimension to retain the original information to a certain extent. Retaining a certain degree means that characteristic differences remain based on the trends in the performance specification data of the vehicle models used as the population. Characteristic differences are reduced in dimension to the extent that they retain the perspectives that users prioritize when choosing a car, for example). In other words, the performance specification data of each vehicle model is mapped by a machine learning model into a vector space of a predetermined dimension (as the characteristic values of each vehicle model) that allows for accurate comparison of the characteristics of individual vehicle models. Furthermore, by being mapped by a machine learning model, the vector space of the characteristic values of each vehicle model can be a (weighted) space that reflects the vehicle's performance (e.g., horsepower or price) that is likely to be prioritized when purchasing a vehicle. In this embodiment, the vectorized characteristic values of each vehicle model enable meaningful calculations between the characteristic values of multiple vehicle models. For example, if the average value (average vector) of the characteristic values of multiple vehicle models is calculated, this average value represents the average characteristics of those multiple vehicle models. Furthermore, the difference between the characteristic value of a specific vehicle model and the average characteristic value of the multiple vehicle models represents the difference between the characteristic of the specific vehicle model and the average characteristic of the multiple vehicle models. Conventionally, when vehicle performance specification data is used directly as characteristic values, it tends to become a high-dimensional vector, and small differences in the performance specification data accumulate, making it difficult to compare vehicle models based on their characteristic differences. In contrast, this embodiment uses data (vehicle characteristic values) in which the performance specification data of each vehicle model is mapped to a vector of a predetermined dimension, enabling highly accurate recommendations based on data that emphasizes the characteristic differences between vehicle models.

Note that the following process is for a specific user. Therefore, if you want to recommend car models to multiple users, you may repeat the process described below for each user.

In S1401, the processor 1302 acquires the attributes of a specific user at a specific time, information about the vehicle model owned at the specific time, and current attributes. For example, the processor 1302 acquires this information about the specific user entered on an information processing terminal included in the automobile recommendation system 1 via the communication interface 1303. Alternatively, the processor 1302 may acquire the user's identifier entered on the information processing terminal, and acquire this information about the specific user from the user's vehicle model 22, user attributes at the start of ownership 23, and current user attributes 24.

In S1402, the processor 1302 determines a first group of users who belong to attributes that are the same as or similar to the attributes of the specific user at a given time. For example, the processor 1302 determines users who have the same attributes (e.g., gender, residential area, and age group) as the specific user at a given time as the users of the first group. The processor 1302, for example, searches for users of the first group from the user attributes 23 at the time of ownership start. The attributes of the specific user at a given time may further include household composition and hobbies.

In S1403, the processor 1302 calculates the characteristic values of the vehicle models requested by the users of the first group. For example, the processor 1302 obtains the vehicle models requested by each user of the first group from the user-owned vehicle models 22, and then obtains the characteristic values of the vehicle models requested by each user of the first group using the characteristic values per vehicle model 31. Furthermore, the processor 1302, for example, calculates the average value of the characteristic values of each obtained vehicle model, and sets this as the characteristic value of the vehicle model requested by the users of the first group. The processing of S1402 and S1403 corresponds to the processing of the user attribute-requested vehicle model characteristic calculation unit 13. The processor 1302 may store the characteristic values of the vehicle models requested by the users of the first group in the user attribute-requested vehicle model characteristics 33. The processing of S1402 and S1403 makes it possible to treat the characteristics of vehicle models requested by multiple users with specific attributes as a single vectorized characteristic value.

At S1404, the processor 1302 obtains the characteristic values of the vehicle model requested by the specific user at the specified time from the information on the vehicle models owned at the specified time. This processing corresponds to the processing of the user-requested vehicle model characteristic calculation unit 12. The processor 302 obtains the characteristic values of the vehicle model requested by the specific user at the specified time from the information on the vehicle models owned by the specific user at the specified time and the characteristic values per vehicle model 31. The processor 1302 may store the characteristic values of the vehicle model for the specific user in the user-requested vehicle model characteristics per user 32.

At S1405, the processor 1302 calculates the difference between the characteristic values of the vehicle model requested by the first group of users and the characteristic values of the vehicle model requested by a specific user at a specific time, and stores this as an index indicating the user's values (in the per-user values 34). This processing corresponds to the processing of the per-user values calculation unit 14. The calculated difference is a vectorized value of a specific dimension, and is therefore expressed as a quantity that can be calculated to indicate the direction and magnitude of the user's values within the space related to the vehicle model characteristics.

At S1406, the processor 1302 determines users in the second group who belong to attributes that are the same as or similar to the current attributes of the specific user. For example, the processor 1302 determines users in the second group who have the same current attributes (e.g., gender, residential area, and age group) as the specific user. At S1407, the characteristics of the vehicle model requested by the users in the second group are added together with an index indicating the specific user's values to calculate the summed vehicle model characteristic value (corresponding to the vehicle model characteristics currently requested by the specific user). For example, the processor 1302 obtains the vehicle model characteristics requested by the users in the second group (from the requested vehicle model characteristics per user attribute 33) and adds them to an index indicating the specific user's values. The value obtained by the addition (characteristic value) is a vectorized value of a predetermined dimension, so the vehicle model characteristics currently requested by the specific user are expressed in a space related to vehicle model characteristics. The processing of S1406 and S1407 corresponds to the processing of the current user requested vehicle model characteristic calculation unit 15. The processor 1302 may store the value (characteristic value) obtained by the addition in the current user-requested vehicle model characteristics 35.

In S1408, the processor 1302 determines a candidate vehicle model (e.g., one with similar characteristics) based on the combined characteristic value of the vehicle model and the characteristic values of each vehicle model (actual characteristic values present in the vehicle model characteristic values 31). For example, the processor 1302 compares the combined characteristic value of the vehicle model with the characteristics of each vehicle model, and extracts, for example, one or more characteristic values of one or more vehicle models that fall within a predetermined Euclidean distance (characteristic values of actual vehicle models that are similar to the combined characteristic value of the vehicle model). The processor 1302 then determines the candidate vehicle model that corresponds to the extracted characteristic value of the vehicle model as the vehicle model to be recommended (candidate vehicle model). Note that if there are multiple candidate vehicle models, the candidate vehicle models may be sorted so that the higher the similarity with the combined characteristic value of the vehicle model, the higher the priority, and vehicle models with higher priority may be displayed at the top.

In S1409, the processor 1302 recommends the candidate vehicle models to the specific user. The processing of S1408 and S1409 corresponds to the processing of the recommended vehicle model calculation unit 16. For example, the processor 1302 may provide a list of candidate vehicle models to an information processing terminal and display the list on the display of the information processing terminal. After executing the processing of S1409, the processor 1302 ends the series of operations of this processing.

As explained above, the automobile recommendation system 1, which is one embodiment of the present invention, is able to recommend automobiles based on the performance indicators of the vehicle models owned by the users, by inferring differences and changes in needs due to differences in user attributes and the values of individual users. By separating the trends of each user's attributes from the values of individual users in this way, it is possible to recommend automobiles according to changes in life stages.

The invention made by the inventor has been specifically described above based on the embodiments, but it goes without saying that the present invention is not limited to the above embodiments and can be modified in various ways without departing from the spirit of the invention. Furthermore, the above embodiments have been described in detail to clearly explain the invention, and the invention is not necessarily limited to those that include all of the described configurations. Furthermore, it is possible to add, delete, or replace some of the configurations of the above embodiments with other configurations.

Furthermore, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits. Furthermore, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in a storage device such as memory, hard disk, or SSD, or on a storage medium such as an IC card, SD card, or DVD.

Furthermore, in each of the above diagrams, the control lines and information lines shown are those considered necessary for explanation, and do not necessarily represent all control lines and information lines in the actual implementation. In reality, it is safe to assume that almost all components are interconnected.

This invention can be used in a car recommendation system that recommends cars.

1...Automotive recommendation system,
11... vehicle model characteristic calculation unit, 12... user-requested vehicle model characteristic calculation unit, 13... user attribute-requested vehicle model characteristic calculation unit, 14... user-value calculation unit, 15... current user-requested vehicle model characteristic calculation unit, 16... recommended vehicle model calculation unit,
21... Performance specifications for each vehicle model, 22... Vehicle model owned by user, 23... User attributes at the time of ownership start, 24... Current user attributes,
31...characteristic value for each vehicle model, 32...vehicle model characteristics required by each user, 33...vehicle model characteristics required by each user attribute, 34...values for each user, 35...vehicle model characteristics required by current user, 36...recommended vehicle model

Claims (5)

A car recommendation system that recommends cars to a user,
a memory for storing instructions;
one or more processors, wherein the instructions, when executed, cause the automobile recommendation system to:
determining a first group of users having attributes that are the same as or similar to attributes of a particular user at a given time;
calculating an index indicating a value of the user from a difference between a characteristic value of the vehicle model required by the first group of users and a characteristic value of the vehicle model required by the specific user at the predetermined time;
determining a second group of users that belong to attributes that are the same as or similar to the current attributes of the particular user;
calculating a combined characteristic value of the vehicle models based on the characteristic values of the vehicle models required by the second group of users and the index indicating the values of the specific user;
determining candidate vehicle models based on the summed characteristic values of the vehicle models and the characteristic values of each vehicle model;
and recommending the candidate vehicle models to the specific user. The automobile recommendation system according to claim 1,
The automobile recommendation system includes determining the candidate vehicle model by determining the characteristic value of a vehicle model that is within a predetermined distance from the combined characteristic value of the vehicle model as the characteristic value of the candidate vehicle model, and determining a vehicle model that has the characteristic value of the candidate vehicle model as the candidate vehicle model. The automobile recommendation system according to claim 1,
determining the candidate vehicle models includes determining a plurality of candidate vehicle models to be displayed on a display based on the combined vehicle model characteristics and characteristics of each vehicle model;
The automobile recommendation system rearranges the plurality of candidate vehicle models so that the higher the similarity with the characteristic values of the combined vehicle model, the higher the priority, and displays vehicle models with higher priority at the top. The automobile recommendation system according to claim 1,
This is an automobile recommendation system in which the characteristic values of vehicle models are data on the performance specifications of vehicle models mapped into a vector space that allows the characteristics of individual vehicle models to be compared. The automobile recommendation system according to claim 1,
When the instructions are executed, the automobile recommendation system:
A car recommendation system that uses a machine learning model to calculate the characteristic values of a vehicle model by mapping data on the vehicle's performance specifications to a feature vector expressed in lower dimensions than the performance specification data.