JP5186012B2 - INFORMATION PROVIDING DEVICE, INFORMATION PROVIDING METHOD, INFORMATION PROVIDING PROGRAM, AND COMPUTER-READABLE RECORDING MEDIUM CONTAINING THE PROGRAM - Google Patents

Description

  The present invention relates to an information providing apparatus, an information providing method, an information providing program, and a computer-readable recording medium on which the program is recorded.

  In recent years, auction services via the Internet have been widely provided. An auction is to make buyers compete for purchase conditions. In an auction via the Internet, a provider providing an auction service prepares a web page for competing for a product. Then, when the exhibitor applies for the sale of the product to the web page, the bidder who is the buyer (the successful bidder) presents the purchase price. Then, the bidder who has presented the highest purchase price by the end date and time can make a successful bid for the product.

  When an item is to be exhibited in an auction, the exhibitor often designates the date and time when the bidder is highly likely to be active in the auction as the end date and time. For example, the exhibitor sets the end date of the auction as a weekend, or sets the end time in the range of 22:00 to 24:00. This is because, in general, there are many people who spend leisure time at such times, and it is highly possible that the number of bids in the auction will increase and the price of the product will rise.

  Conventionally, methods related to determination of the end time have been considered. For example, in Patent Document 1 below, information including bid statistical information for each product name is stored, and when an exhibition request is acquired from the seller's device via the network, the stored bid statistical information for each product name is used. A net auction server for determining the end time of a net auction is described.

JP 2005-196418 A

  In the server described in Patent Literature 1, the end time is determined based on the bid statistical information for each item. Further, the user's behavior that is the basis of the statistical information is limited to bidding, and other access modes to the auction are not considered. For this reason, it is difficult for the server to accurately obtain end times for all products in an actual auction service that handles a great variety of products.

  The present invention has been made in view of such a situation, and an object thereof is to provide useful information for determining the end time to an exhibitor who is trying to determine the end time of an auction.

The information providing apparatus according to the present invention can store an action time information indicating a time when a participant of an auction has accessed the auction in the past from a storage means for an auction of a product belonging to the category of the product to be exhibited by the exhibitor. Corresponding action time information is extracted, and based on the extracted action time information, the estimation means for estimating the time when the number of participants in the auction is a predetermined threshold or more as the access concentration prediction time, and the seller's terminal, Providing means for providing data indicating the estimated access concentration estimated time estimated by the estimating means, and when the estimating means detects the listing of the goods by the seller, the other goods belonging to the category of the goods Identify participants who are either bidding, registering to watchlists, or registering reminder emails, Extract the action time information.

The information providing method according to the present invention is an information providing method executed by the information providing apparatus, and includes an exhibitor from storage means for storing action time information indicating a time when a participant of the auction has accessed the auction in the past. Extracts action time information corresponding to an auction for a product belonging to the category of the product to be listed, and based on the extracted action time information, predicts a time when the number of participants in the auction is equal to or greater than a predetermined threshold. An estimation step for estimating as a time, and a providing step for providing data indicating the access concentration prediction time estimated in the estimation step to the seller's terminal, wherein the estimation step detects the listing of the product by the seller. , Bid for other products belonging to the category of the product, registration in the watch list, and remind Identify the participants have done any of the registration of Lumpur, to extract the action time information corresponding to the identified participants.

The information providing program according to the present invention is a product that belongs to the category of the product that the exhibitor intends to exhibit from the storage means that stores the action time information indicating the time when the auction participant accessed the auction in the past. Extracting the action time information corresponding to the auction, and estimating the access concentration prediction time as a time when the number of participants in the auction is equal to or greater than a predetermined threshold based on the extracted action time information;
Let the seller's terminal function as a providing means for providing data indicating the estimated access concentration estimated time estimated by the estimating means, and the estimating means belongs to the category of the product at the time when the seller detects the listing of the product. bid on other items, registered to the watch list, and to identify the participants who are doing one of remind mail of registration, it extracts the action time information corresponding to the identified participants.

The computer-readable recording medium according to the present invention includes a category of a product that an exhibitor intends to exhibit from a storage unit that stores behavior time information indicating a time when an auction participant previously accessed the auction. An estimation means for extracting action time information corresponding to an auction of a product belonging to, and estimating a time when the number of participants in the auction is equal to or greater than a predetermined threshold based on the extracted action time information as an access concentration prediction time; Let the seller's terminal function as a providing means for providing data indicating the estimated access concentration estimated time estimated by the estimating means, and the estimating means belongs to the category of the product at the time when the seller detects the listing of the product. Perform bidding, watch list registration, and remind mail registration for other products To identify the participants in, has recorded to that information providing program extracts the action time information corresponding to the identified participants.

According to such an invention, a predetermined number or more of participants are expected based on action time information corresponding to an auction of a product that belongs to the category of the product that the seller intends to sell (hereinafter also referred to as “exhibited product”). Is estimated as an access concentration prediction time (hereinafter, also simply referred to as “prediction time”). By providing the predicted time to the seller, the seller can determine the end date and time in consideration of the predicted time. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction. Further, in the information providing apparatus according to the present invention, when the exhibitor sells the product, the estimation means bids, registers in the watch list, and registers the remind mail for other products belonging to the product category. By identifying the participants who are doing any of the above, and extracting the behavior time information corresponding to the identified participants, the behavior time information of the participants who are expected to participate in the auction of the exhibited product is appropriately Can be extracted.

  In the information providing apparatus according to the present invention, the estimation unit specifies the number of participants per hour based on the extracted action time information, and the time when the specified number of participants is equal to or greater than a threshold is set as the access concentration prediction time. It may be estimated.

  In this case, the number of participants per hour is specified, and the time when a certain number of participants or more are expected is estimated as the predicted time. By providing the predicted time to the seller, the seller can determine the end date and time in consideration of the predicted time. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction.

In the information providing apparatus according to the present invention, the action time information includes information indicating the attributes of the participants, and the time for which the number of participants in the auction is equal to or greater than a predetermined threshold for each category of the product and the attributes of the participants. Aggregation means for generating aggregated action total information is further provided, and the estimation means corresponds to the category of the product exhibited by the exhibitor from the behavior total information, and the attribute of the participant indicated by the extracted action time information May be extracted, and the extracted time may be estimated as the access concentration prediction time.

  In this case, based on the attributes of the participants specified from the action time information, a time when a certain number of participants are expected is estimated as the predicted time. By providing the predicted time to the seller, the seller can determine the end date and time in consideration of the predicted time. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction.

In the information providing apparatus according to the present invention, the action time information includes information indicating the attributes of the participants, and the time for which the number of participants in the auction is equal to or greater than a predetermined threshold for each category of the product and the attributes of the participants. Aggregating means for generating aggregated action totaling information is further provided, and the estimating means is configured to perform hourly based on the extracted action time information when the exhibiting period of the product that the seller intends to exhibit is shorter than a predetermined period. identify the number of participants, estimates the time the number of participants that have been identified is equal to or larger than the threshold as access concentration estimated time, when the exhibition period is equal to or more than a predetermined time period, from the behavior aggregate information, seller exhibition It is also possible to extract a time corresponding to the category of the product to be performed and corresponding to the attribute of the participant indicated by the extracted action time information, and estimating the extracted time as the access concentration prediction time.

  When the exhibition period is short, the present inventors tend to participate in the auction, and when the exhibition period is long, the entire user having the same attribute as the identified participant. We obtained the knowledge that the general participation tendency is close to the participation tendency in a specific auction. In the information providing apparatus as described above, when the exhibition period of the product is shorter than the predetermined period, the number of participants for each hour is specified, and the time when more than a certain number of participants are expected is estimated as the predicted time. . On the other hand, when the exhibition period is equal to or longer than the predetermined period, the attribute of the participant is specified from the behavior time information, and based on the attribute, the time when a certain number of participants are expected is estimated as the predicted time. Thereby, useful information for determining the end time can be provided to the exhibitor who intends to determine the end time of the auction.

  In the information providing apparatus according to the present invention, the action time information further includes user evaluation information indicating the evaluation of the participant, and the providing means refers to the user evaluation information related to the participant when there are a plurality of access concentration prediction times. The priority for providing the plurality of access concentration prediction times may be determined based on the evaluation of the participants.

  In this case, since the priority of provision of the predicted time is determined based on the evaluation of the participant, the exhibitor can determine the end date and time in consideration of the predicted time reflecting the evaluation of the participant. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction.

  ADVANTAGE OF THE INVENTION According to this invention, the useful information for determining the end time can be provided to the exhibitor who is going to determine the end time of an auction.

It is a figure which shows the structure of the information provision system which concerns on 1st Embodiment. It is a block diagram which shows the hardware constitutions of the server which concerns on 1st Embodiment. It is a block diagram which shows the function structure of the server which concerns on 1st Embodiment. It is a figure which shows the example of action time information. It is a flowchart which shows the process which updates an evaluation value. It is a figure which shows a response | compatibility with an evaluation value and an evaluation stage. (A), (b) is a figure which shows the example of estimation of prediction time, respectively. It is a sequence diagram which shows the prediction time estimation process in 1st Embodiment. It is a block diagram which shows the function structure of the server which concerns on 2nd Embodiment. It is a flowchart which shows the process which memorize | stores action total information. It is a figure which shows the example of action total information. It is a figure which shows the example of action total information. (A)-(c) is a figure which shows the example of estimation of prediction time, respectively. It is a sequence diagram which shows the prediction time estimation process in 2nd Embodiment. It is a figure which shows another example of action total information. It is a block diagram which shows the function structure of the server which concerns on 3rd Embodiment. It is a sequence diagram which shows the prediction time estimation process in 3rd Embodiment.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the present embodiment, the information providing apparatus according to the present invention is applied to a server. In the description of the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant description is omitted.

(First embodiment)
First, an information providing system according to the first embodiment will be described. The information providing system is a computer system that provides auction services to users, and includes a server 11, the Internet 12, and one or more user terminals 13 as shown in FIG. The server 11 and each user terminal 13 can communicate with each other via the Internet 12.

  The server 11 is an information providing device including a dedicated server, a personal computer, or a virtual server. The server 11 may be a system composed of a combination of a dedicated server, a personal computer, or a virtual server. The server 11 provides an auction service to the user of the user terminal 13 by providing the user terminal 13 with a web page for auction. In addition, the server 11 estimates the time when the number of bidders equal to or greater than a predetermined threshold is predicted for a product that has been exhibited in the auction by the seller as a predicted time (access concentration predicted time), and data indicating the predicted time is stored for each item. This is provided to the user terminal 13.

  The Internet 12 is an example of a network, and includes a wired or wireless general-purpose line or dedicated line, or a plurality of wired or wireless networks (for example, a LAN (Local Area Network) or a WAN (WIDeArea Network)).

  The user terminal 13 obtains and displays a web page for auction from the server 11 according to the user's operation, thereby presenting the web page to the user of the user terminal 13. As a result, the user can operate the web page to place a product for auction or bid. Examples of the user terminal 13 include a portable or stationary personal computer and various portable terminals, but the type of terminal is not limited at all.

  Next, the server 11 will be described in detail. FIG. 2 is a block diagram illustrating a hardware configuration example of the server 11. In the server 11, a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, and a RAM (Random Access Memory) 33 are connected to each other by a bus 34. An input / output interface 35 is further connected to the bus 34. The input / output interface 35 includes an input unit 36 including a keyboard, a mouse, and a microphone, an output unit 37 including a display and a speaker, a storage unit 38 including a hard disk and a nonvolatile memory, and a communication unit 39 including a network interface. A drive 40 for driving a removable medium 41 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory is connected.

  In the server 11 (computer) configured as described above, the CPU 31 loads, for example, the information providing program stored in the storage unit 38 to the RAM 33 via the input / output interface 35 and the bus 34 and executes it. As a result, a series of processing described later is performed.

  The information providing program is stored on a removable medium 41 such as a magnetic disk (including a flexible disk), an optical disk (such as a CD-ROM (Compact Disc-Read Only Memory) or a DVD (Digital Versatile Disc)), a magneto-optical disk, or a semiconductor memory. May be provided. The information providing program may be provided through a wired or wireless transmission medium such as a local area network, the Internet 12, or digital satellite broadcasting.

  The information providing program can be installed in the computer by mounting the removable medium 41 in the drive 40 and storing it in the storage unit 38 via the input / output interface 35. In addition, the information providing program can be installed in a computer by being received by the communication unit 39 via a wired or wireless transmission medium and stored in the storage unit 38. Further, the information providing program can be installed in advance by storing it in the ROM 32 or the storage unit 38 in advance.

  The information providing program may be a program that causes the computer to perform processing in time series according to the order described below, or may be performed in parallel or at a necessary timing such as when a call is made. It may be a program that performs processing.

  FIG. 3 is a block diagram illustrating a functional configuration of the server 11. The server 11 includes a Web server function 51, a page generation unit 52, a service providing unit 53, a predicted time providing unit 54, a user database 55, an auction related database (storage unit) 56, and an auction history database (storage unit) 57. Yes.

  The web server function 51 is realized by executing a predetermined web server program, and provides a web page including various objects such as text or images based on a procedure defined in HTTP (Hypertext Transfer Protocol). . The web page is described in HTML (Hypertext Markup Language) or XML (Extensible Markup Language). The web server function 51 includes a transmission unit 71 and a reception unit 72. The transmission unit 71 transmits the web page to the user terminal 13. The receiving unit 72 receives various data transmitted from the user terminal 13.

  The page generation unit 52 generates a web page to be provided to the user terminal 13 and outputs the web page to the transmission unit 71 of the web server function 51. This web page also includes a web page for presenting the estimated time to the exhibitor.

  The service providing unit 53 accepts events indicating various actions in the auction and login and logout for the auction service. This event indicates a user ID for identifying a user who participates in the auction and information indicating the user's behavior (login, logout, bid, successful bid, exhibition, etc.).

  The predicted time providing unit 54 estimates a time during which a predetermined number or more users are expected to bid on the exhibited product. The predicted time providing unit 54 provides the predicted time to the seller by providing the predicted time (core time) to the seller's user terminal 13 via the page generating unit 52 and the web server function 51. Here, the predicted time may be a time zone in units of 30 minutes, 1 hour, 2 hours, etc. in one day, such as “Sunday 12:00 to 13:00”, etc. It may be a time zone that further considers the day of the week. What prediction time is provided may be arbitrarily determined.

  The user database 55 stores data (user data) related to users who use the auction service. The user data is data in which a user ID for identifying a user and various attributes such as the user's name, address, telephone number, e-mail address, age, gender, and login password are associated with each other.

  The auction related database 56 stores information related to auctions (auction information). The auction information includes an auction ID for specifying an auction, a user ID of the exhibitor, a product ID for specifying the exhibited product, the name and category of the product, the start price at the auction, and the end of the auction. It includes a date and time, a user ID that specifies a user (bidder) who bids on the product, a bid date and time, a user ID that specifies a successful bidder of the product, and a successful bid date and time. Since bidding may occur multiple times for one product, one auction information may include user IDs and bidding dates and times of a plurality of bidders. The category of the product means the type of the product. For example, home appliances, interiors, household goods, foods, and the like correspond to the category. The categories may be expressed hierarchically such as “food—sweets—confectionery—chocolate”.

  The auction information further includes a user ID that identifies the user who registered the auction in the watch list, and a user ID that identifies the user who registered the remind mail for the auction. The user who registers the auction in the watch list can be said to be a user who is interested or interested in the auction.

  It can be said that the various user IDs included in the auction information are participant IDs that identify participants of the auction.

  The auction history database 57 stores action time information indicating the time when each user accesses the auction.

  As shown in FIG. 4, the action time information includes a user ID that identifies a user who participated in the auction, the age and sex of the user, the action history of the user (successful bid history, bid history, and stay history), The evaluation value of the user is included. The successful bid history is information indicating a product ID for specifying a product for which a user has made a successful bid, a name (product name) of the product, and a successful bid date and time. The bid history is information indicating the ID and name of a product bid by the user and the bid date and time. The stay history is information indicating the date and time when the user logged into the auction service and the date and time when the user logged out from the service. Such an action history indicates that the user has accessed the auction. The evaluation value is user evaluation information indicating the evaluation of the other person with respect to the user's behavior in the auction service, and the larger the value, the more preferable the user is as a trading partner. The initial value of the evaluation value is 0. The evaluation stage is user evaluation information set corresponding to the evaluation value, and is used to roughly indicate the evaluation by dividing the evaluation value step by step for each predetermined width.

  From the action time information of the user indicated by the user ID “0001” shown in FIG. 4, for example, the user has made a successful bid for three types of products indicated by the product IDs “A0001”, “C0201”, and “T0211”, respectively. I understand. It can also be seen that the user bid for the product indicated by the product ID “A0001” at 21:55, 22:30, and 22:40 on Sunday, January 17. Further, it can be seen that the user has been using the auction service for 40 minutes from 22:10 on Saturday, January 16th. In the example of FIG. 4, the user's evaluation value is 12 points (“D” when converted to the evaluation stage).

  Returning to FIG. 3, details of the predicted time providing unit 54 will be described. The predicted time providing unit 54 includes a recording unit 73, an extracting unit (estimating unit) 74, and an estimating unit (estimating unit, providing unit) 75.

  The recording unit 73 updates the action time information in the auction history database 57. The recording unit 73 includes an action time recording unit 73a and an evaluation value recording unit 73b.

  The action time recording unit 73a refers to the event received by the service providing unit 53 and records various user histories. When the event indicates login to the auction service or logout from the auction service, the action time recording unit 73a adds the login date / time or the logout date / time to the stay history of the corresponding action time information of the user. When the event indicates a successful bid for the product, the action time recording unit 73a adds a record including the ID and name of the product and the successful bid date and time to the successful bid history of the corresponding user action time information. If the event indicates a bid for a product, the action time recording unit 73a adds a record including the ID and name of the product and the bid date and time to the bid history of the corresponding user action time information.

  The evaluation value recording unit 73b updates the user's evaluation value. As shown in FIG. 5, the evaluation value recording unit 73b acquires the evaluation values of the seller and the successful bidder set by the supplier providing the auction service by the server 11 (S1). In this case, the evaluation value recording unit 73b may acquire the evaluation value from a predetermined database of the supplier. Subsequently, the evaluation value recording unit 73b acquires the evaluation value of the winning bidder set by the exhibitor (S2). Furthermore, the evaluation value recording unit 73b acquires the seller's evaluation value set by the successful bidder (S3). In steps S2 and S3, the evaluation value recording unit 73b receives an evaluation value input by a user via a predetermined web page or receives an e-mail in which the evaluation value is described by the user. Get the evaluation value. Subsequently, the evaluation value recording unit 73b adds the acquired evaluation value of the seller or successful bidder to the evaluation value of the corresponding action time information in the auction history database 57, thereby updating the evaluation value of the person. (S4).

  The evaluation value recording unit 73b refers to the evaluation value / evaluation stage correspondence table illustrated in FIG. 6 and registers the evaluation stage corresponding to the updated evaluation value in the action time information. This correspondence table is held in advance in the evaluation value recording unit 73b.

  Returning to FIG. 3, the extraction unit 74 refers to the auction related database 56 and the auction history database 57 to extract the user ID (participant ID) of the user who participated in the auction of the product belonging to the category of the exhibited product. The extraction unit 74 includes a search unit 74a and a participant extraction unit 74b.

  The search unit 74a acquires category information indicating the category of the exhibited product transmitted from the seller's user terminal 13. Subsequently, the search unit 74a extracts from the auction-related database 56 auction information of an auction in which other products belonging to the category indicated by the category information are listed and for which a bid is currently accepted.

  The participant extraction unit 74b extracts the user IDs (participant IDs) of the users participating in the auction searched by the search unit 74a at the time when the seller has exhibited the product. The participant extraction unit 74b may extract various user IDs included in the auction information extracted by the search unit 74a as participant IDs. But the kind of participant ID extracted by the participant extraction part 74b is not limited. For example, the participant extraction unit 74b may extract only the bidder's user ID as the participant ID, or extract only the bidder's user ID and the user ID of the user who registered the auction in the watch list as the participant ID. May be. The participant extraction unit 74b outputs the extracted participant ID to the estimation unit 75.

  The estimation unit 75 estimates the time when many bids are expected in the auction of the exhibited product as the predicted time, and transmits the predicted time to the user terminal 13. The estimation unit 75 includes a candidate extraction unit 75a and a time determination unit 75b.

  The candidate extraction unit 75a reads behavior time information corresponding to the participant ID extracted by the participant extraction unit 74b from the auction history database, and based on the read behavior time information, it is predicted that many users will participate in the auction. Time candidates (prediction time candidates, hereinafter also referred to as “candidate times”).

  First, the candidate extraction unit 75a extracts action time information whose user ID matches the participant ID from the auction history database 57, and calculates the number of users per hour based on the extracted action time information. For example, the candidate extraction unit 75 calculates the number of users for each predetermined time period during the day. Subsequently, the candidate extraction unit 75a estimates a candidate for a time when a predetermined number of users or more are predicted to participate in the auction for the exhibited product based on the number of users per hour. The following shows some estimation methods.

  The candidate extraction unit 75a may estimate the candidate time based on the number of participants. This method will be described with reference to FIG. In the example of FIG. 7A, the participants are limited to bidders for the sake of simplicity. The crosses in FIG. 7 indicate the time when a bid was placed in an auction for another product belonging to the category of the exhibited product. In the example shown in FIG. 7A, two people are bidding at 20: 00 to 21:00, four people are bidding at 21: 00 to 22:00, and 22: 00 to 23: Two people are bidding at 00, no one is bidding at 23: 00 to 0:00, and one is bidding at 0: 00 to 1:00. The candidate extraction unit 75a obtains the number of participants per time as shown in FIG. 7A by counting the bid history of the extracted action time information.

  Then, candidate extraction part 75a sets up candidate time based on the number of participants for every acquired time. Specifically, the candidate extraction unit 75a may set a time “21:00 to 22:00” with the largest number of participants as a candidate time (see prediction time candidate A). In this case, it can be said that the condition “the number of participants is the most” is the threshold value. Further, the candidate extraction unit 75a may set a time when the number of participants is equal to or greater than a predetermined threshold as the candidate time. For example, when the threshold is set to 2, the candidate extraction unit 75a sets 20:00 to 23:00 having two or more participants as candidate times (see prediction time candidate B). Here, the predetermined threshold value may be indicated by the absolute number of participants, or may be indicated by the ratio of the number of participants to the total number of users who have logged in to the auction at a predetermined time.

  The candidate extraction unit 75a may estimate the candidate time based on the participation frequency of each user. This method will be described with reference to FIG. In the example of FIG. 7B as well, participants are limited to bidders for the sake of simplicity. The crosses in FIG. 7A also indicate the time at which a bid was placed in an auction for another product belonging to the category of the exhibited product. In the example of FIG. 7B, the average bidding frequency of 2 users who bid between 20: 00 to 21:00 is 5 times a week, and the average of 4 users who bid between 21: 00 to 22:00. It shows that the bidding frequency is twice a week. The average bidding frequency is also shown at other times. The candidate extraction unit 75a aggregates the bid history of the extracted action time information to obtain an average bid frequency for each hour as shown in FIG.

  Subsequently, the candidate extraction unit 75a sets a candidate time based on the acquired average bid frequency for each hour. In this case, the candidate extraction unit 75a may perform weighting by multiplying the number of bidders by the average bid frequency, and may set a time with the highest weighting result as the candidate time. For example, in the case of FIG. 7B, the candidate extraction unit 75a sets a time “22: 0 to 23:00” in which the weighting result is 2 × 10 = 20 as the candidate time (see candidate C of the prediction time). ). Further, the candidate extraction unit 75a may set a time with the highest average bid frequency as a candidate time. The reason for considering the bid frequency in this way is that it is estimated that the time during which a user with a high bid frequency acts is more likely to be actively bid in the auction of the exhibited product.

  When there are a plurality of acquired candidate times, the candidate extraction unit 75a may further narrow down the candidate times by further referring to the evaluation value of each user. For example, the candidate extraction unit 75a may set the time with the largest number of participants (hereinafter also referred to as “highly rated”) whose evaluation value or evaluation stage is equal to or higher than a predetermined threshold as the final candidate time. . In addition, the candidate extraction unit 75a may set the time when the ratio of the number of high-evaluators to the total number of users logged in to the auction at a predetermined time is the highest as the final candidate time. Or the candidate extraction part 75a may set time with the highest ratio of the number of high evaluation persons with respect to the total number of participants in predetermined time as a final candidate time. In addition, the candidate extraction unit 75a may set a time when the result obtained by multiplying the number of participants by a weight set in advance for each evaluation stage is the maximum candidate time.

  The threshold value for the evaluation value or the evaluation stage may be set in any way. For example, 70 may be set as the evaluation value threshold, or “B” may be set as the evaluation stage threshold.

  In the example of FIG. 7, the candidate extraction unit 75a estimates the prediction time based on the bid history, but may similarly estimate the candidate time based on the successful bid history or the stay history. Moreover, the candidate extraction part 75a may estimate candidate time similarly by totaling all action history.

  The candidate extraction unit 75a outputs the candidate time estimated (set) as described above to the time determination unit 75b.

  The time determination unit 75b determines the predicted time to be presented to the exhibitor. Several methods for this determination are shown below.

  The time determination unit 75 b may determine the input one or more candidate times as they are as one or more prediction times, and output the prediction times to the page generation unit 52. In this case, the time determination unit 75b rearranges a plurality of candidate times in descending order of the number of high-evaluators or descending order of the rate of high-evaluators, and then uses these candidate times as predicted times to the page generation unit 52. It may be output. In this case, the predicted time with a large number of high-evaluators or the predicted time with a high ratio of high-evaluators is preferentially presented to the exhibitor.

  In addition, the time determination unit 75b calculates, for each hour, the number of auctions in which other products belonging to the category of the exhibited product are listed, and determines the predicted time based on the number of ends and the input candidate time. May be. Specifically, the time determination unit 75 b extracts auction information indicating an auction in which another product belonging to the category of the exhibited product is exhibited from the auction related database 56. Subsequently, the time determination unit 75b calculates the number of auction ends for each hour based on the end date and time of the extracted auction information. Subsequently, for each candidate time, the time determination unit 75b calculates the ratio of the number of participants at that time to the number of completed auctions at that time, and determines the time at which the ratio is maximum as the predicted time. Then, the time determination unit 75 b outputs the determined predicted time to the page generation unit 52. Alternatively, the time determination unit 75b may set priorities for each candidate time in descending order of the ratio, and then output these candidate times to the page generation unit 52 as predicted times.

  For example, assume that the number of completed auctions and the number of participants in the first candidate time are both 10, and the number of completed auctions and the number of participants in the second candidate time are 5 and 10, respectively. In this case, since the ratio between the first and second candidate times is 1 and 2, respectively, the time determination unit 75b outputs the second candidate time as the predicted time. Alternatively, the time determination unit 75b sets a priority higher than the first time to the second candidate time, and then outputs the first and second candidate times to the page generation unit 52 as predicted times.

  Next, prediction time estimation processing (information providing method) in the present embodiment will be described with reference to FIG. First, the seller's user terminal 13 obtains category information indicating the category of the exhibited product (S1001). Specifically, the user terminal 13 acquires the category information selected from the pull-down menu on the web page that accepts the auction listing, or the category information directly input. Subsequently, the user terminal 13 transmits the category information to the server 11 (S1002).

  In the server 11, the receiving unit 72 receives the category information and outputs it to the predicted time providing unit 54 (S2001).

  In the predicted time providing unit 54, the search unit 74a searches for an auction for a product belonging to the category indicated by the category information and accepting the current bid (S2002). Subsequently, the extraction unit 74 selects a predetermined number of auctions from the searched auctions (S2003). For example, the extraction unit 74 may randomly select 10 pieces of data from the searched auctions, or may select all the searched auctions. Subsequently, the participant extraction unit 74b extracts the user ID (participant ID) of the participant in the auction based on the auction information of the selected auction (S2004, estimation step).

  Subsequently, the estimation unit 75 estimates the prediction time. Specifically, the candidate extraction unit 75a extracts action time information corresponding to the extracted participant ID from the auction history database 57, and counts the number of users for each time based on the action time information. And the candidate extraction part 75a estimates prediction time based on the number of users for every time (S2005, estimation step). As described above, several prediction methods in this case can be considered. Subsequently, the prediction time is finally estimated by determining the time that the time determination unit 75b presents to the exhibitor from the candidate time (S2005, estimation step). As described above, several determination methods are also conceivable.

  Subsequently, the page generation unit 52 generates a web page including the predicted time (S2006). The page generation unit 52 may generate a web page indicating the number of bidders per hour as shown in FIG. 7, for example, but the display method of the predicted time on the web page is not limited at all. Subsequently, the transmission unit 71 of the web server function 51 transmits the web page to the user terminal 13 (S2007, providing step).

  The user terminal 13 receives the web page transmitted from the server 11 (S1003), and displays the web page (S1004).

  Thus, the exhibitor can confirm the predicted time displayed as shown in FIG. 7, for example, and determine an appropriate end time. For example, the exhibitor can determine a specific time within the time (predicted time) in which many bidders are expected as the end time.

  As described above, according to the present embodiment, the number of participants per hour is specified based on the action time information corresponding to the auction of other products belonging to the category of the exhibited product, and a certain number of participants or more The expected time is estimated as the predicted time. By providing the predicted time to the seller, the seller can determine the end date and time in consideration of the predicted time. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction.

(Second Embodiment)
Next, an information providing system according to the second embodiment will be described. This information providing system is the first embodiment in that the prediction time is presented based on the participation status of all auctions, not the participation status of the auction that is related to the same category product as the exhibited product and currently accepting bids. It differs from the information provision system concerning. For this purpose, the information providing system of this embodiment includes a server 11 </ b> A instead of the server 11. Hereinafter, description of the same or equivalent items as those in the first embodiment will be omitted.

  FIG. 9 shows a functional configuration of the server 11A. The server 11 </ b> A is different from the server 11 in that it further includes an action time counting unit 73 c in the recording unit 73 and further includes an attribute specifying unit 74 c in the extraction unit 74. Accordingly, the operations of some functional components are also different from those of the first embodiment.

  The action time totaling unit 73c is activated at a predetermined time and performs the process shown in FIG. First, the behavior time totaling unit 73c reads the behavior time information of each user from the auction history database 57 (S11), and classifies the behavior time information by attribute (S12). The classification method is not limited. For example, the action time totaling unit 73c may classify the action time information by age group or sex, or may classify the action time information by sex and age group.

  Subsequently, the action time totaling unit 73c extracts the time when many successful bids are made for each attribute as the successful bid time (S13). There are several methods for extracting the successful bid time. For example, the action time totaling unit 73c may extract the time with the largest number of successful bids as the successful bid time for each attribute. In this case, the condition “the largest number of successful bids” is the threshold value. Moreover, the action time totaling unit 73c may extract, as a successful bid time, a time when the number of successful bids is a predetermined threshold (for example, 10,000) or more for each attribute. Alternatively, the action time totaling unit 73c may extract, as the successful bid time, for each attribute, the time when the ratio of the number of successful bids at a specific time with respect to the total number of successful bids in the attribute is equal to or greater than a predetermined threshold (for example, 30%). Good.

  Subsequently, the action time totaling unit 73c extracts the day of the week when many successful bids are made for each attribute as the successful bid day (S14). There are several possible methods for extracting successful bid days. For example, the action time totaling unit 73c may extract the day of the week with the highest number of successful bids as the successful bid day for each attribute. Moreover, the action time totaling unit 73c may extract a day of the week when the number of successful bids is a predetermined threshold (for example, 30,000) or more as a successful bid day for each attribute. Alternatively, the action time totaling unit 73c may extract, as the successful bid day, for each attribute, the day of the week in which the ratio of the number of successful bids on a specific day to the total number of successful bids in the attribute is equal to or greater than a predetermined threshold (for example, 30%). Good.

  Subsequently, the action time totaling unit 73c stores the successful bid time and the successful bid day in the auction history database 57 as action total information for each attribute (S15). In addition, the storage location of action total information is not limited to this example.

  Further, the action time totaling unit 73 c may generate action totaling information for each category of merchandise exhibited in the auction and for each attribute of the participant, and store it in the auction history database 57. In this case, the action time totaling unit 73c classifies the action time information extracted from the auction history database 57 by category and by participant attribute. Subsequently, the action time totaling unit 73c extracts the time when many successful bids are made for each category and for each attribute as the successful bid time.

  Examples of action total information are shown in FIGS. The action total information shown in FIG. 11 is action total information when totaling by age group. For example, from this behavior summary information, users belonging to the age group of 18 to 29 years old tend to make a successful bid between 22:00 and 24:00, and also tend to make a successful bid on Friday, Saturday, and Sunday. I understand that. On the other hand, the action total information shown in FIG. 12 is action total information when totaling by sex and age group. The setting of the age group and the setting of the time are not limited to the examples in FIGS. 11 and 12 and may be arbitrarily determined. For example, the action time counting unit 73c may set the age group width to 5 (years old) or may count the number of successful bids every hour. If a plurality of attributes are taken into consideration as in the example of FIG. 12, the prediction time can be estimated in more detail. When the action total information is generated for each product category and for each participant attribute, the total results as shown in FIGS. 11 and 12 are obtained for each product category.

  Returning to FIG. 9, the extraction unit 74 refers to the auction related database 56 and the auction history database 57 to extract attributes of users participating in the auction of other products belonging to the category of the exhibited product.

  The attribute specifying unit 74c extracts action time information corresponding to the participant ID extracted by the participant extracting unit 74b from the auction history database 57, and specifies the attribute of the participant based on the action time information. Thereby, the attribute specific | specification part 74c acquires the attribute of the user who participated in the auction of other goods whose category is the same as exhibition goods. Note that the type of attribute specified here is the same as the type of attribute processed in the action time totaling unit 73c. Of course, the attribute specifying unit 74c may acquire a plurality of attribute values for one type of attribute. For example, the attribute specifying unit 74c may acquire two attribute values “30 to 39 years old” and “40 to 49 years old” regarding the age group. The attribute specifying unit 74 c outputs the acquired attribute information to the estimation unit 75.

  The estimation unit 75 estimates the time when many bids are expected in the auction of the exhibited product as the predicted time, and transmits the predicted time to the user terminal 13.

  The candidate extraction unit 75a refers to the action time information or action total information in the auction history database 57 and estimates a predicted time candidate. There are several possible estimation methods.

  As a first method, the candidate extraction unit 75a may estimate the predicted time using only the action summary information. First, the candidate extraction unit 75a reads the action total information corresponding to the attribute of the participant input from the attribute specifying unit 74c from the auction history database 57. Subsequently, based on the successful bid time and successful bid day for each attribute indicated by the action total information, the candidate extraction unit 75a estimates the time and day of the week when many bids are predicted to be made in the auction.

  For example, on the premise of the action total information shown in FIG. 11, four age groups “18 to 29 years”, “30 to 39 years”, “40 to 49 years”, and “50 to 59 years” are input from the attribute specifying unit 74c. Suppose. In this case, the candidate extracting unit 75a makes a successful bid time “22:00 to 24:00” for the age group “18 to 29 years” and a successful bid time “21:00 to 23:23” for the age group “30 to 39 years”. 00, the successful bid time “21: 0 to 23:00” of the age group “40 to 49 years old”, and the successful bid time “20:00 to 22:00” of the age group “50 to 59 years old” are estimated as candidate times. (See FIG. 13A). Further, the candidate extraction unit 75a estimates a successful bid day corresponding to the input attribute (for example, age group) as a candidate time. The process related to the successful bid day is the same as the process related to the successful bid time.

  As a second method, the candidate extraction unit 75a may estimate the candidate time using the action total information and the action time information. First, the candidate extraction unit 75a acquires the successful bid time for each attribute as in the first method. Subsequently, the candidate extraction unit 75a extracts, from the auction history database 57, action time information for which the attribute matches and the bid date / time corresponds to the successful bid time for each set of attribute and successful bid time. Subsequently, the candidate extraction unit 75a obtains the number of bidders by attribute and by successful bid time based on the extracted action time information.

  For example, as shown in FIG. 13B, the number of bidders corresponding to the successful bid times of the age groups “18 to 29 years”, “30 to 39 years”, “40 to 49 years”, and “50 to 59 years” is 4 respectively. , 6, 4 and 2. In this case, the candidate extraction unit 75a estimates “22:00 to 23:00”, which has the largest total number of bidders, as a candidate time (see prediction time candidate D).

  In addition, the candidate extraction unit 75a estimates a day of the week with the highest total number of bidders as a candidate time among successful bid days corresponding to the input attribute (for example, age group). The process related to the successful bid day is the same as the process related to the successful bid time.

  Further, the candidate extraction unit 75a may count the number of bidders for each day of the week and for each hour, and estimate a specific time on a specific day of the week with the largest number as the candidate time.

  Further, the candidate extraction unit 75a may hold the candidate time for each attribute shown in FIG. 13B together with the number of bidders corresponding to the candidate time. In this case, the number of bidders at each predicted time is also displayed on the web page provided to the seller.

  In this case, the number of persons may be displayed for each evaluation stage (evaluation value) as shown in FIG. Thus, the exhibitor can select a time when there is a high possibility that the seller will win a successful bid. For example, in the example of FIG. 13C, the exhibitor can determine the end time by targeting the time from 21:00 to 22:00, which is likely to be awarded to the user of the evaluation stage “A”.

  If the highest priority is to make a bid for a bidder with a higher evaluation value, in the example of FIG. 13C, the candidate extraction unit 75a shows only users in the evaluation stage “A”. The time from 1:00 to 21:00 may be extracted.

  The candidate extraction unit 75a outputs the candidate time estimated as described above to the time determination unit 75b. The time determination unit 75b estimates the prediction time as in the first embodiment.

  Next, prediction time estimation processing (information providing method) in the present embodiment will be described with reference to FIG. The processes of steps S1101, S1102, and S2101 to S2104 are the same as the processes of steps S1001, S1002, and S2001 to S2004 in the first embodiment.

  Subsequently, the action time information corresponding to the extracted participant ID is extracted from the auction history database 57 by the attribute specifying unit 74c, and the attribute of the participant is specified based on the action time information (S2105, estimation step).

  Subsequently, the estimation unit 75 estimates the prediction time. Specifically, the candidate extraction unit 75a extracts behavior total information corresponding to the specified attribute from the auction history database 57, and estimates a predicted time based on the behavior total information (S2106, estimation step). As described above, several prediction methods in this case can be considered. Subsequently, the time determination unit 75b determines the predicted time from the candidate time (S2106, estimation step).

  Subsequent processes in steps S2107, S2108, S1103, and S1104 are the same as the processes in steps S2006, S2007, S1003, and S1004 in the first embodiment.

  As described above, according to the present embodiment, a time when a certain number of participants are expected is estimated as the predicted time based on the attributes of the participants specified from the action time information. By providing the predicted time to the seller, the seller can determine the end date and time in consideration of the predicted time. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction.

  In this embodiment, action total information as shown in FIG. 15 may be used. That is, the action time totaling unit 73c calculates not only the successful bid time and the successful bid day, but also the time with the highest number of bids (bid time) and the time with the largest number of user stays (stay time) for each gender and age group, You may produce | generate action total information containing such information. Although FIG. 15 shows only the action total information for men, the action total information for women is generated in the same manner. When the action total information is generated for each product category and for each participant attribute, a total result as shown in FIG. 15 is also obtained for each product category.

  In this case, the candidate extraction unit 75a extracts a successful bid time, a successful bid day, a bid time, and a stay time for each attribute based on the action total information. Subsequently, the candidate extraction unit 75a extracts candidate times for each attribute based on the extracted information and the attributes of each participant extracted using the participant ID.

(Third embodiment)
Next, an information providing system according to the third embodiment will be described. This information providing system is different from the information providing systems according to the first and second embodiments in that the processing method of the predicted time is selected based on the exhibition period of the product designated by the exhibitor. For this purpose, the information providing system of this embodiment includes a server 11B instead of the servers 11 and 11A. Hereinafter, description of the same or equivalent matters as in the first and second embodiments will be omitted.

  The functional configuration of the server 11B is shown in FIG. The server 11B is different from the server 11A in that the extraction unit 74 further includes a technique determination unit 74d.

  The technique determination unit 74d acquires the exhibition period transmitted from the user terminal 13 of the exhibitor, and determines the estimation method of the predicted time based on the exhibition period. When the exhibition period is shorter than a predetermined period (for example, one week), the technique determining unit 74d determines to estimate the prediction time by the same technique as in the first embodiment, and when the exhibition period is equal to or longer than the predetermined period. Determines to estimate the prediction time by the same method as in the second embodiment. Based on this determination, the extraction unit 74 and the estimation unit 75 estimate the prediction time using either the method of the first embodiment or the second embodiment.

  The prediction time estimation process (information provision method) in this embodiment is demonstrated using FIG. First, the user terminal 13 acquires the category and the exhibition period of the exhibited product input by the exhibitor (S1201). The exhibition period is acquired when the exhibitor inputs an end date and time on a web page that accepts an auction entry. Subsequently, the user terminal 13 transmits the acquired category information and the exhibition period to the server 11B (S1202).

  In the server 11, the receiving unit 72 of the Web server function 51 receives the category information and the exhibition period (S2201). Subsequent processes in steps S2202 to S2204 are the same as the processes in steps S2002 to S2004 in the first embodiment.

  Subsequently, the technique determining unit 74d determines whether or not the exhibition period is equal to or longer than a predetermined period (S2205, estimation step). In this case, when the exhibition period is shorter than the predetermined period (S2205; NO), the predicted time is estimated based on the number of participants for each time (S2206, NO) as in the process of step S2005 in the first embodiment. Estimation step). On the other hand, if the exhibition period is equal to or longer than the predetermined period (S2205; YES), the attributes of the participants are specified from the extracted participant IDs (S2207, YES) as in the procedures of S2105 and S2106 in the second embodiment. (Estimation step), the prediction time is estimated based on the attribute (S2208, estimation step). Subsequent steps S2209, S2210, S1203, and S1204 are the same as steps S2006, S2007, S1003, and S1004 in the first embodiment.

  As described above, according to the present embodiment, when the exhibition period of the product is shorter than the predetermined period, the number of participants per hour is specified, and the time when a certain number of participants are expected is estimated time Is estimated as On the other hand, when the exhibition period is equal to or longer than the predetermined period, the attribute of the participant is specified from the behavior time information, and the time when a certain number of participants or more are expected is estimated based on the attribute as the predicted time. That is, it is possible to provide useful information for determining the end time to the exhibitor who is trying to determine the end time of the auction.

  When the exhibition period is short, the identified participants tend to participate in the auction. On the other hand, when the exhibition period is long, the overall participation tendency of users having the same attributes as the specified participant is often applied to the participation tendency in a specific auction. Therefore, it is effective to change the estimation time estimation method as described above according to the length of the exhibition period.

  The present invention has been described in detail based on the embodiments. However, the present invention is not limited to the above embodiment. The present invention can be variously modified without departing from the gist thereof.

  In each of the above-described embodiments, the extraction unit 74 extracts the auction participants in which other products belonging to the category of the exhibited product are listed, but may extract only the auction participants of the same product as the exhibited product. Good.

  In the above embodiments, the servers 11, 11A, and 11B are provided with various databases 55, 56, and 57. However, these databases 55, 56, and 57 may be provided outside the server. In this case, the server may access these databases 55, 56, and 57 via a predetermined communication network. Similarly, the Web server function 51, the page generation unit 52, and the service providing unit 53 may be arranged on another server other than the servers 11, 11A, and 11B.

  In each of the above embodiments, the action time information includes three types of action histories (bid history, successful bid history, stay history), but only one or two of these histories are included in the action time information. It may be. Moreover, the action history included in the action time information is not limited to the above, and may be arbitrarily determined.

  In each of the above embodiments, the evaluation value is included in the action time information, but such user evaluation information can be omitted. In this connection, the evaluation value may not be used when estimating the prediction time. The estimation unit 75 may estimate the prediction time using only one of the evaluation value and the evaluation stage.

  In the second and third embodiments, the candidate time is extracted based on the attribute of the participant, and the predicted time is estimated based on the candidate time. However, the action total information is classified according to the category of the product and the attribute of the participant. When generated, the candidate time may be extracted based on the attribute indicated by the action total information corresponding to the category of the exhibited product, and the predicted time may be estimated based on the candidate time.

  DESCRIPTION OF SYMBOLS 11, 11A, 11B ... Server (information provision apparatus), 12 ... Internet, 13 ... User terminal, 51 ... Web server function, 52 ... Page generation part, 53 ... Service provision part, 54 ... Predictive time provision part, 55 ... User Database, 56 ... Auction related database (storage means), 57 ... Auction history database (storage means), 71 ... Transmission section, 72 ... Reception section, 73 ... Recording section, 73a ... Action time recording section, 73b ... Evaluation value recording section 73c ... Action time totaling unit, 74 ... Extraction unit (estimating means), 74a ... Searching unit, 74b ... Participant extracting unit, 74c ... Attribute specifying unit, 74d ... Method determining unit, 75 ... Estimating unit (estimating means, providing) Means), 75a ... candidate extraction unit, 75b ... time determination unit.

Claims (8)

The action time information corresponding to the auction of the product belonging to the category of the product to be exhibited by the seller is extracted from the storage means for storing the action time information indicating the time when the participant of the auction has accessed the auction in the past. , An estimation means for estimating a time when the number of participants in the auction is equal to or greater than a predetermined threshold based on the extracted action time information as an access concentration prediction time;
Providing means for providing data indicating the predicted access concentration time estimated by the estimating means to the terminal of the seller ;
When the estimation means detects the listing of a product by the seller, it performs any of bidding, registration in a watch list, and registration of a remind mail for another product belonging to the category of the product. wherein identifying the participants, the action time information information providing apparatus that to extract corresponding to the identified participants.   The estimation means specifies the number of participants per hour based on the extracted action time information, and estimates the time when the specified number of participants is equal to or greater than the threshold as the access concentration prediction time. Item 4. The information providing apparatus according to Item 1. The action time information includes information indicating an attribute of the participant,
For each category of product and the attribute of the participant, further comprises a counting means for generating action totaling information by counting the time when the number of participants in the auction is equal to or greater than a predetermined threshold,
The estimation means extracts, from the action total information, a time corresponding to the category of the product exhibited by the exhibitor and corresponding to the attribute of the participant indicated by the extracted action time information. time for the estimated as the access concentration prediction time, the information providing apparatus according to claim 1. The action time information includes information indicating an attribute of the participant,
For each category of product and the attribute of the participant, further comprises a counting means for generating action totaling information by counting the time when the number of participants in the auction is equal to or greater than a predetermined threshold,
The estimating means is
When the exhibition period of the product to be exhibited by the seller is shorter than a predetermined period, the number of participants per time is specified based on the extracted action time information, and the number of specified participants Is estimated as the access concentration prediction time,
When the exhibition period is equal to or longer than the predetermined period , the attribute of the participant corresponding to the category of the product exhibited by the exhibitor and indicated by the extracted action time information from the action total information The information providing apparatus according to claim 1, wherein a time corresponding to the information is extracted, and the extracted time is estimated as the access concentration prediction time. The action time information further includes user evaluation information indicating a participant's evaluation,
When the providing means has a plurality of access concentration prediction times, the providing means refers to user evaluation information regarding the participant and determines the priority of providing the plurality of access concentration prediction times based on the evaluation of the participant The information providing apparatus according to any one of claims 1 to 4 . An information providing method executed by an information providing apparatus,
The action time information corresponding to the auction of the product belonging to the category of the product to be exhibited by the seller is extracted from the storage means for storing the action time information indicating the time when the participant of the auction has accessed the auction in the past. An estimation step for estimating a time when the number of participants in the auction is equal to or greater than a predetermined threshold based on the extracted action time information as an access concentration prediction time;
Providing the seller's terminal with data indicating the estimated access concentration time estimated in the estimating step ; and
In the estimation step, at the time of detecting the listing of the product by the seller, any of the products belonging to the category of the product is bid, registered in the watch list, or registered in the remind mail. An information providing method for identifying the participant and extracting the action time information corresponding to the identified participant . Computer
The action time information corresponding to the auction of the product belonging to the category of the product to be exhibited by the seller is extracted from the storage means for storing the action time information indicating the time when the participant of the auction has accessed the auction in the past. , An estimation means for estimating a time when the number of participants in the auction is equal to or greater than a predetermined threshold based on the extracted action time information as an access concentration prediction time;
Allowing the seller's terminal to function as providing means for providing data indicating the estimated access concentration time estimated by the estimating means ;
When the estimation means detects the listing of a product by the seller, it performs any of bidding, registration in a watch list, and registration of a remind mail for another product belonging to the category of the product. said to identify the participants, the information providing program that extracts the action time information corresponding to the identified participants. Computer
The action time information corresponding to the auction of the product belonging to the category of the product to be exhibited by the seller is extracted from the storage means for storing the action time information indicating the time when the participant of the auction has accessed the auction in the past. , An estimation means for estimating a time when the number of participants in the auction is equal to or greater than a predetermined threshold based on the extracted action time information as an access concentration prediction time;
Allowing the seller's terminal to function as providing means for providing data indicating the estimated access concentration time estimated by the estimating means ;
When the estimation means detects the listing of a product by the seller, it performs any of bidding, registration in a watch list, and registration of a remind mail for another product belonging to the category of the product. wherein identifying the participants, identified you extracted the action time information corresponding to the participant information providing program recorded with computer-readable recording medium.

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