JP2021174269A - Hostel proposal program - Google Patents

Abstract

To automatically and highly accurately propose an optimum hostel in accordance with service content and accommodation fees desired by a guest.SOLUTION: A hostel proposal program for proposing a hostel to be actually reserved to a guest from among candidates of each hostel included in a place specified by the guest, causes a computer to execute: an information acquisition step of acquiring service content information and accommodation fees information desired by the guest; and a proposal step of referring to a combination having reference service content information on service content of each hostel and reference accommodation fees information on the accommodation fees of each hostel, and an association degree of three or more level with the hostels to output the hostel to be proposed based on the reference service content information in accordance with the service content acquired in the information acquisition step and the reference accommodation fees information in accordance with the acquired accommodation fees information.SELECTED DRAWING: Figure 3

Description

The present invention relates to an accommodation proposal program for determining the rate of increase / decrease in accommodation charges.

Accommodation charges for hotels, inns, etc. vary greatly depending on the time of day, month, summer vacation, Golden Week, year-end and New Year holidays, etc. In addition to these timing factors, accommodation charges vary depending on the external environment such as the spread of infectious diseases, international affairs, social affairs, and political affairs. , The accommodation fee changes.

In this way, accommodation charges vary depending on the external environment, events, and transportation, in addition to time factors. In addition to this, the accommodation charges desired by guests who wish to stay at accommodations vary widely, so the relationship between the accommodation charges of guests and the actual accommodation charges is consistent with each other. Searching for conditions and proposing them to guests is a cumbersome task. For this reason, a system that can automatically perform these operations and propose an accommodation facility that meets the guest's price has been desired. In addition to this, there are also service contents desired by individual guests (for example, whether or not they have an open-air bath, whether or not they have breakfast, whether or not they have room massage, etc.), and whether or not they have such service contents. It is necessary to propose the most suitable accommodations including the above, but such work is also complicated work, and a system that can propose accommodations that meet the customer's service content has been desired.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose of the present invention is to automatically and highly accurately obtain the optimum accommodation according to the service content and accommodation fee desired by the guest. The purpose is to provide an accommodation proposal program that can be proposed to.

In order to solve the above-mentioned problems, the accommodation proposal program according to the present invention proposes to the guest the accommodation to be actually reserved from the candidates for each accommodation included in the place designated by the guest. In the accommodation proposal program, the information acquisition step to acquire the service content information regarding the service content desired by the guest and the accommodation fee information regarding the accommodation fee, the reference service content information regarding the service content of each of the above accommodations, and each of the above accommodations. With reference to the combination having the reference accommodation fee information regarding the accommodation fee of the place and the reference service content information according to the service content information acquired in the above information acquisition step by referring to the degree of association with the accommodation in three or more stages. It is characterized in that the computer executes a proposal step of outputting the accommodation to be proposed based on the reference accommodation charge information according to the acquired accommodation charge information.

Even if you do not have any special skills or experience, it is possible to automatically and accurately propose the most suitable accommodation according to the service content and accommodation fee desired by the guest.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the accommodation proposal program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of an accommodation proposal system 1 in which an accommodation proposal program to which the present invention is applied is implemented. The accommodation proposal system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data on the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

Database 3 stores various information necessary for making accommodation proposals. Information necessary for making accommodation proposals includes reference service content information regarding the service content of each of the above accommodations, reference accommodation charge information regarding the accommodation charges of each of the above accommodations, and attributes of guests who have stayed in the past. Reference attribute information, reference accommodation history information regarding past accommodation history of guests who stayed in the past, reference external environment information regarding past external environment, reference market condition information regarding past market conditions, accommodations held in the past The reference promotional event information about the promotional event and the data set with the accommodation are stored.

That is, in the database 3, in addition to such reference service content information and reference accommodation fee information, reference attribute information, reference accommodation history information, reference external environment information, reference market condition information, and reference sales promotion event Any one or more of the information and the accommodation are stored in association with each other. By the way, the accommodations judged in the past set not only the data actually judged in the past but also a fictitious persona, and the accommodations that each institution or trader would propose about this are used as learning data. May be included.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation of the accommodation proposal system 1 having the above-described configuration will be described.

The accommodation proposal system 1 proposes to the guest the accommodation to be actually reserved from the candidates for each accommodation included in the place designated by the guest. When booking an accommodation, guests usually specify a location, and among the accommodation candidates included in the area of that location, the accommodation that best suits their financial and service content. Select. Therefore, when implementing the accommodation proposal system 1, it is premised that the candidates for each accommodation included in the location designated by the guest are narrowed down. In addition to the location, the guest usually specifies the actual date of stay, but it is premised that the candidates are narrowed down according to the specified date. That is, the accommodation proposal system 1 according to the present invention implements the selection of the most suitable accommodation from the candidates for accommodation in which the place and the date of stay are narrowed down in advance.

In the accommodation proposal system 1, it is premised that a combination of the reference service content information and the reference accommodation fee information is formed, for example, as shown in FIG. The reference service content information is all information regarding the content of the service provided by the accommodation, for example, whether or not there is a large communal bath or open-air bath, whether or not there is a massage service, whether or not breakfast is included, and check-in and check-out times. , Whether or not there is a dinner show, the contents, whether or not there is a daycare center, and the degree of cleaning facilities. Here, accommodation includes all facilities where accommodation is possible, and also includes hotels, inns, youth hostels, simple inns, capsule hotels, private lodgings, and guesthouses.

The reference accommodation fee information is information on the accommodation fee provided by each accommodation. The accommodation fee referred to here may include not only the accommodation fee but also the meal fee and the service fee. The accommodation fee specified in the reference accommodation fee information may be composed of the average value of the accommodation charges provided by the accommodation, or may be composed of the accommodation charges set for each room. Further, it may be indicated by the degree of increase / decrease of the accommodation fee acquired in chronological order over a certain period, or may be composed of the rate ratio with other accommodations, etc., instead of the accommodation fee itself.

As the input data, such reference service content information and reference accommodation fee information are arranged side by side. The intermediate node shown in FIG. 3 is a combination of the reference service content information and the reference accommodation fee information as such input data. Each intermediate node is further linked to the output. In this output, the accommodation as the output solution is displayed.

Each combination (intermediate node) of the reference service content information and the reference accommodation fee information is associated with each other through three or more levels of association with the accommodation as this output solution. The reference service content information and the reference accommodation fee information are arranged on the left side through this degree of association, and each accommodation is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to which accommodations A to E with respect to the reference service content information and the reference accommodation fee information arranged on the left side. In other words, this degree of association is an index indicating what kind of accommodation the reference service content information and the reference accommodation fee information are likely to be associated with, and the reference service content information and the reference accommodation. It shows the accuracy in selecting the most probable accommodation from the price information. In the example of FIG. 3, w13 to w22 are shown as the degree of association. These w13 to w22 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the accommodation as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the accommodation as an output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates reference service content information, reference accommodation fee information, and a data set in which the accommodation is linked to these information in order to determine the actual accommodation, and these By analyzing and analyzing the above, the degree of association shown in FIG. 3 is created. Such a data set is obtained by extracting the accommodation charges provided by each accommodation in the past and the information on the service contents from the information on the Internet and the like.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference service content information P01 and the reference accommodation fee information P16, what kind of accommodation is actually associated with such information is analyzed from the past data. For example, in the example of the intermediate node 61a to which the reference service content information P01 and the reference accommodation fee information P16 are connected, the output of the accommodation A and the accommodation B is linked. Since there are many cases where such service contents and accommodation fees are adopted, the degree of association of w13 connected to accommodation A is set to 7 points, and the degree of association of w14 connected to accommodation B is set to 2 points. ..

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. That is, the reference service content information regarding the service content of each accommodation and the reference accommodation fee information regarding the accommodation fee of each accommodation are stored in association with each of the above accommodations. Then, the combination of the stored reference service content information and the reference accommodation fee information and the above accommodation are used as teacher data, the input is the reference service content information and the reference accommodation fee information, and the output is the accommodation. The judgment model is generated by machine learning. After creating such learned data, when actually determining a new accommodation at a railroad crossing, the accommodation will be determined using the above-mentioned learned data. In such a case, the service content information is newly acquired and the accommodation fee information is acquired.

The newly acquired service content information and accommodation fee information are acquired through the user interface via the mouse, touch panel, and keyboard in the information acquisition unit 9 described above.

Based on the newly acquired service content information and the accommodation fee information in this way, the accommodation facility at the time when the newly acquired service content information and the accommodation fee information are actually acquired is obtained. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired service content information is the same as or similar to P02, the most recently arrived accommodation charge information at the time of acquisition is the same as or similar to the reference accommodation charge information P17. In the case, the node 61d is associated with each other through the degree of association, and the accommodation C is associated with the accommodation C at the degree of association w19 and the accommodation D is associated with the accommodation D with the degree of association w20. In such a case, accommodation C having a higher degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the accommodation D, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

In FIG. 4, in addition to the above-mentioned reference service content information and reference accommodation fee information, a combination of reference attribute information and three or more levels of association with the accommodation for the combination are set. An example is shown. Reference attribute information is any information about the guest's attributes. Examples of this reference attribute information are, for example, age, gender, occupation, annual income, whether or not the work has a lot of business trips, family information, hometown information, and the like. Such attribute information should be obtained from a database managed by each accommodation, which records information on attributes that guests who have made a reservation in the past at each accommodation or who have stayed in the past at the time of check-in or reservation. You may.

In such a case, as shown in FIG. 4, the degree of association is such that the set of combinations of the reference service content information, the reference accommodation fee information, and the reference attribute information is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 4, the reference service content information P02 is associated with the reference degree w3, the reference accommodation fee information P15 is associated with the association degree w7, and the reference attribute information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference service content information P03 is associated with the association degree w5, the reference accommodation fee information P15 is associated with the association degree w8, and the reference attribute information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, attribute information is newly acquired in addition to the newly acquired service content information and accommodation fee information. This attribute information, like the service content information and the accommodation fee information, is acquired through the information acquisition unit 9, which is information to be acquired from the guest who is going to propose an accommodation.

Based on the reference attribute information associated with the attribute defined in the acquired attribute information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this accommodation, the degree of association shown in FIG. 4 acquired in advance is referred to. For example, the acquired service content information is the same as or similar to the reference service content information P02, the acquired accommodation fee information corresponds to the reference accommodation fee information P15, and the reference attribute information corresponding to the attribute of the acquired attribute information. Corresponds to P21, the combination is associated with a node 61c, in which accommodation B is associated with a degree of association w17 and accommodation D is associated with a degree of association w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In FIG. 5, in addition to the above-mentioned reference service content information and reference accommodation fee information, a combination of reference accommodation history information and three or more levels of association with the accommodation for the combination are set. An example is shown. The reference accommodation history information is all information regarding the guest's past accommodation history. As an example of this reference accommodation history information, for example, accommodation A has stayed 4 times in the past 5 years, accommodation B has stayed 2 times in the past 5 years, and accommodation E has stayed 8 times in the past 5 years. It may be, or the history up to the day of each stay may be recorded. In addition to this, the accommodation tendency of the accommodation from the past to the present may be organized as a history. For example, in the past 3 to 5 years, the frequency of staying at accommodation A was high, but within the past 3 years, the frequency of staying at accommodation C is high, which is expressed as one accommodation tendency. There may be.

In such a case, as shown in FIG. 5, the degree of association is such that the set of combinations of the reference service content information, the reference accommodation fee information, and the reference accommodation history information is the nodes 61a to 61e of the intermediate nodes as described above. Will be expressed as.

For example, in FIG. 5, in the node 61c, the reference service content information P02 is associated with the association degree w3, the reference accommodation fee information P15 is associated with the association degree w7, and the reference accommodation history information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference service content information P03 is associated with the association degree w5, the reference accommodation fee information P15 is associated with the association degree w8, and the reference accommodation history information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, the accommodation history information is newly acquired in addition to the newly acquired service content information and accommodation fee information. This accommodation history information is acquired through the information acquisition unit 9, which is information to be acquired from a guest who is about to propose an accommodation, like the service content information and the accommodation fee information.

Based on the reference accommodation history information associated with the attributes defined in the acquired accommodation history information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this accommodation, the degree of association shown in FIG. 5 acquired in advance is referred to. For example, the acquired service content information is the same as or similar to the reference service content information P02, the acquired accommodation fee information corresponds to the reference accommodation fee information P15, and the reference accommodation corresponding to the attribute of the acquired accommodation history information. When the history information corresponds to P21, the combination is associated with the node 61c, and the accommodation B is associated with the association degree w17 and the accommodation D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In FIG. 6, in addition to the above-mentioned reference service content information and reference accommodation fee information, a combination of reference external environment information and three or more levels of association with the accommodation are set for the combination. An example is shown. The reference external environment information is various information related to the external environment information. The external environmental information here is economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption situation survey, household data, average working hours per week, savings amount). Statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, wind direction data, humidity data, etc.) Etc.). External environmental information may include the presence or absence and extent of various events related to infectious diseases, natural disasters, politics, economy, and society. The external environmental information includes all information outside the accommodation, in addition to the information that reflects some or all of these data. The external environment information for reference may be categorized as the external environment itself.

In such a case, as shown in FIG. 6, the degree of association is such that the set of combinations of the reference service content information, the reference accommodation fee information, and the reference external environment information is the same as described above, and the nodes 61a to 61e of the intermediate node. Will be expressed as.

For example, in FIG. 6, the reference service content information P02 is associated with the reference degree w3, the reference accommodation fee information P15 is associated with the association degree w7, and the reference external environment information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference service content information P03 is associated with the association degree w5, the reference accommodation fee information P15 is associated with the association degree w8, and the reference external environment information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, external environment information is newly acquired in addition to the newly acquired service content information and accommodation fee information. This external environment information acquires the latest external environment via the information acquisition unit 9.
Based on the reference external environment information associated with the attributes defined in the acquired external environment information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this accommodation, the degree of association shown in FIG. 6 acquired in advance is referred to. For example, the acquired service content information is the same as or similar to the reference service content information P02, the acquired accommodation fee information corresponds to the reference accommodation fee information P15, and the reference external corresponding to the attribute of the acquired external environment information. When the environmental information corresponds to P21, the combination is associated with the node 61c, and the accommodation B is associated with the association degree w17 and the accommodation D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In FIG. 7, in addition to the above-mentioned reference service content information and reference accommodation fee information, a combination of reference market condition information and three or more levels of association with the accommodation for the combination are set. An example is shown.

Reference market information is various information related to market conditions. The market conditions referred to here may target any range including one company, the entire industry including the company, the entire Japan, and the entire world. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, MACD, moving average line and the like may be attached. In addition, this market information may include fundamental indicators of each brand's company, and in addition to annual sales, profits, and costs, PER (price-earnings ratio), PBR (price-to-book value ratio), ROE ( Indicators such as return on equity) may be included. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving averages may be attached to the exchange rate. The reference market information may categorize the market potential itself. That is, the reference market information in FIG. 10 may be information classified by type, and for example, it may be classified by dividing it according to whether or not the growth rate of the stock price is 0% or more per year. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of the stock price increases rapidly or gradually) or the like.

In such a case, as shown in FIG. 7, the degree of association is such that a set of combinations of reference service content information, reference accommodation fee information, and reference market condition information is set as nodes 61a to 61e of intermediate nodes as described above. It will be expressed.

For example, in FIG. 7, the reference service content information P02 is associated with the reference degree w3, the reference accommodation fee information P15 is associated with the association degree w7, and the reference market condition information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference service content information P03 is associated with the association degree w5, the reference accommodation fee information P15 is associated with the association degree w8, and the reference market condition information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, market condition information is newly acquired in addition to the newly acquired service content information and accommodation fee information. This market condition information acquires the latest market condition through the information acquisition unit 9.

Based on the reference market condition information associated with the attributes defined in the acquired market condition information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this accommodation, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, the acquired service content information is the same as or similar to the reference service content information P02, the acquired accommodation fee information corresponds to the reference accommodation fee information P15, and the reference market condition information corresponding to the attribute of the acquired market condition information. Corresponds to P21, the combination is associated with a node 61c, in which accommodation B is associated with a degree of association w17 and accommodation D is associated with a degree of association w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In FIG. 8, in addition to the above-mentioned reference service content information and reference accommodation fee information, a combination of reference sales promotion event information and three or more levels of association with the accommodation are set for the combination. An example is shown.

The reference promotional event information is various information related to the promotional event. The promotional event here is a plan to invite famous talents to dinner shows, to serve local specialties for dinner at a super-discount price, to invite only elderly people aged 65 and over at a low price, and even more. Courses that include special projects for Christmas and Halloween can be considered. Such reference sales promotion event information may be categorized by a pattern or the like of the actual sales promotion event held at each accommodation.

In such a case, as shown in FIG. 8, the degree of association is such that the set of combinations of the reference service content information, the reference accommodation fee information, and the reference sales promotion event information is the nodes 61a to 61e of the intermediate node as described above. Will be expressed as.

For example, in FIG. 8, the reference service content information P02 is associated with the reference degree w3, the reference accommodation fee information P15 is associated with the association degree w7, and the reference sales promotion event information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference service content information P03 is associated with the reference degree w5, the reference accommodation fee information P15 is associated with the association degree w8, and the reference sales promotion event information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, in addition to the newly acquired service content information and accommodation fee information, newly acquired sales promotion event information is acquired. This sales promotion event information acquires the latest sales promotion event of each accommodation through the information acquisition unit 9.

Based on the reference sales promotion event information associated with the attribute defined in the acquired sales promotion event information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this accommodation, the degree of association shown in FIG. 8 acquired in advance is referred to. For example, the acquired service content information is the same as or similar to the reference service content information P02, the acquired accommodation fee information corresponds to the reference accommodation fee information P15, and the reference sales promotion corresponding to the attribute of the acquired sales promotion event information. When the event information corresponds to P21, the combination is associated with the node 61c, in which the accommodation B is associated with the association degree w17 and the accommodation D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In addition to service content information and accommodation fee information, if any two or more of attribute information, accommodation history information, external environment information, market condition information, and sales promotion event information are acquired, the information to be acquired depends on the two or more information. Also, refer to two or more reference information (reference attribute information, reference accommodation history information, reference external environment information, reference market information, reference sales promotion event information, etc.), reference service content information, reference accommodation fee information By creating learning data consisting of the combination with and the degree of association with the accommodation in the combination, the solution search can be performed in the same manner.

In addition to service content information and accommodation fee information, in addition to any one or more of attribute information, accommodation history information, external environment information, market information, sales promotion event information, etc., the same applies when acquiring other information. You can search for a solution.

Further, as shown in FIG. 9, the present invention determines the rate of increase / decrease of each accommodation fee based on the degree of association between two or more types of information, reference information U and reference information V. It is assumed that the reference information U is the reference service content information, and the reference information V is the reference accommodation fee information. Alternatively, it is assumed that the reference information U is the reference accommodation fee information and the reference information V is the reference service content information.

At this time, as shown in FIG. 9, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the rate of increase / decrease in the accommodation fee of a company considering financing without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since the above-mentioned input data and output data often do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

Candidates for the possibility of search solutions by determining the rate of increase / decrease in accommodation charges, which is more credible and less misleading, based on the degree of association expressed by such numerical values of three or more levels. It is also possible to search and display in descending order of the degree of association under the situation where there are a plurality of possible situations.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to service content information and accommodation fee information, when knowledge, information, and data related to attribute information, accommodation history information, external environment information, market information, and sales promotion event information are acquired, the degree of association is increased accordingly. Or lower it.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Accommodation proposal system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (8)

In the accommodation proposal program that proposes to the guest the accommodation that should actually be reserved from the candidates for each accommodation included in the place designated by the guest.
Information acquisition step to acquire service content information related to the service content desired by the guest and accommodation fee information related to the accommodation fee, and
Refer to the combination of the reference service content information regarding the service content of each of the above accommodations and the reference accommodation charge information regarding the accommodation charges of each of the above accommodations, and the degree of association with the above accommodations at three or more levels. Based on the reference service content information according to the service content information acquired in the information acquisition step and the reference accommodation fee information according to the acquired accommodation fee information, the proposal step that outputs the accommodation to be proposed is output to the computer. An accommodation proposal program characterized by being executed. In the above information acquisition step, the attribute information of the above guest is acquired, and
In the determination step, there are three or more stages of the combination of the reference service content information, the reference accommodation fee information, and the reference attribute information regarding the attributes of the guests who have stayed in the past, and the accommodation. The accommodation proposal program according to claim 1, wherein the accommodation to be proposed is output based on the reference attribute information according to the attribute information acquired in the above information acquisition step with reference to the degree of association. In the above information acquisition step, the accommodation history information regarding the accommodation history of the above guest is acquired, and the accommodation history information is acquired.
In the determination step, the combination of the reference service content information, the reference accommodation fee information, and the reference accommodation history information regarding the past accommodation history of the guest who stayed in the past, and the accommodation. The accommodation according to claim 1, wherein the accommodation to be proposed is output based on the reference accommodation history information according to the accommodation history information acquired in the above information acquisition step by referring to the degree of association of three or more stages. Accommodation proposal program. In the above information acquisition step, the external environment information about the external environment is acquired, and the information is acquired.
In the determination step, the above-mentioned combination having the above-mentioned reference service content information, the above-mentioned reference accommodation fee information, and the above-mentioned reference external environment information regarding the past external environment, and the degree of association with the above-mentioned accommodation in three or more stages are determined. The accommodation proposal program according to claim 1, wherein the accommodation to be proposed is output based on the reference external environment information according to the external environment information acquired in the above information acquisition step. In the above information acquisition step, market information regarding market conditions is acquired, and
In the above-mentioned determination step, the above-mentioned combination having the above-mentioned reference service content information, the above-mentioned reference accommodation fee information, and the above-mentioned reference market condition information regarding the past market conditions, and the degree of association with the above-mentioned accommodation in three or more stages are referred to. The accommodation proposal program according to claim 1, wherein the accommodation to be proposed is output based on the reference market condition external environment information according to the market condition information acquired in the above information acquisition step. In the determination step, the above-mentioned combination having the above-mentioned reference service content information, the above-mentioned reference accommodation fee information, and the above-mentioned reference sales promotion event information regarding the sales promotion event of the accommodation held in the past, and the above-mentioned accommodation 3 The accommodation according to claim 1, wherein the accommodation to be proposed is output based on the reference sales promotion event information according to the service content information acquired in the above information acquisition step by referring to the degree of association of the stage or higher. Proposal program. The accommodation fee according to any one of claims 1 to 6, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. Proposal program. In the accommodation proposal system that proposes to the guest the accommodation that should actually be reserved from the candidates for each accommodation included in the place designated by the guest.
A storage means for storing reference service content information regarding the service content of each of the above accommodations and reference accommodation charge information regarding the accommodation charges of each of the above accommodations in association with each of the above accommodations, respectively.
The combination of the reference service content information and the reference accommodation fee information stored in the above storage means and the above accommodation are used as teacher data, the input is the reference service content information and the reference accommodation fee information, and the output is the accommodation. A model generation means that generates a judgment model to be used by machine learning,
Reception means for accepting input of service contents and accommodation charges desired by guests, and
Using the determination model generated by the model generation means, it is estimated from the reference service content information according to the service content input to the reception means and the reference accommodation fee information according to the input accommodation fee. An accommodation proposal system characterized by having a processing means for outputting information about the accommodation.

Images