KR20110065103A - System and method for estimation product of on-line sale, and apparatus applied to the same - Google Patents

Abstract

PURPOSE: A system and a method for evaluating a product and device for applying the same on online sales are provided to enable other clients to utilize the provided information as purchase information about the product by providing evaluation information of a client about the product sold on online as objective information. CONSTITUTION: An online sales server(200) provides commodity information about the commodity through a WAP or a web page for selling a product. A customer terminal device(100) supports an online shopping of a client. A product evaluation server(300) forms client activity distinguishing information based on activity history about an online shopping of client. The product evaluation server produces reliability coefficient about customer valuation information through client activity distinguishing information.

Description

SYSTEM AND METHOD FOR ESTIMATION PRODUCT OF ON-LINE SALE, AND APPARATUS APPLIED TO THE SAME}

The present invention relates to an e-commerce scheme, and more particularly, to reform and provide customer valuation information on products sold online as undistorted, objective information so that other customers can use them as purchase information for products. The present invention relates to a system and method for evaluating merchandise in online sales, and an apparatus applied thereto.

In recent years, with the advance of technology of computer and communication network, the exchange and collection of information is made easy and affects various fields of society, and the electronic based on the so-called internet world wide web In response to the trend of widespread use of commerce, various trading methods are actively being promoted.

As the situation changes, companies open their own websites on the Internet to provide product information to customer terminal devices such as customers' computers, and operate virtual shopping malls that can deliver ordered products to customers.

In the home shopping that purchases products in this shopping mall, the user terminal device is connected to the online sales server of the website, and then a user clicks with a mouse referring to the product information (for example, the price of the product, the specification of the product, the field of use, etc.). By purchasing the desired items through the operation, the distribution stage of the product is reduced compared to the classic commerce, which directly goes to the market and purchases the product, thereby making it possible to purchase the product at a simple and low cost.

Recently, when a customer who wants to purchase a product presents a purchase price, a commodity trading service company that runs a so-called 'reverse auction' that connects sellers who have responded to the price is running a website on the Internet.

In addition, the website that combines the advantages of home shopping using a shopping mall and additional discounts for mass purchases is also operating a website for arranging a so-called 'joint purchase'.

As such, although a general customer may use the service in a cyber shopping space that trades products based on the Internet, a method for increasing the utilization rate by providing additional benefits to a subscription member that is separately subscribed is prepared.

Furthermore, in the online shopping based on the Internet, there is a disadvantage in that the user does not check directly like the offline transaction market until the product is purchased and delivered.

Therefore, after confirming the product evaluation information started by the previous purchaser as the user's review of the product, the purchaser determines whether to purchase the corresponding product by judging according to its own criteria.

However, even in this manner, it is not possible to guarantee whether the product evaluation information disclosed by the previous buyer is objectified information without any distortion, and thus a countermeasure is required.

Accordingly, the present invention was created to solve the above problems, and an object of the present invention is to provide an object that is not distorted so that other customers can use the customer's evaluation information on products sold online as purchase information on products. The present invention provides a system and method for evaluating a product in online sales for remodeling and providing information, and an apparatus applied thereto.

In addition, another object of the present invention is to provide information to help buyers who purchase goods online through the purchase decision of the product, on the basis of this to enable the creation of additional services that are differentiated from other online shopping To provide a system and method for evaluating merchandise in sales, and an apparatus applied thereto.

In order to achieve the above object, a product evaluation system in an online sale according to a first aspect of the present invention includes an online sales server that provides product information on the product through a web page or a swap for selling the product online. A customer terminal device for supporting a customer's online shopping including reading of product information, purchasing of the product, and forming customer evaluation information, and forming customer activity discrimination information based on the activity history of the customer's online shopping After that, the confidence coefficient for the customer evaluation information is calculated through the customer activity discrimination information, and the product evaluation information including the confidence coefficient and the customer evaluation information is formed and transmitted to the online sales server. And a product evaluation server for displaying the product evaluation information on the page. Shall be.

Preferably, the product evaluation system in the on-line sales is implemented as a server integrating the on-line sales server and the product evaluation server, or characterized in that implemented separately from the online sales server and the product evaluation server.

Preferably, when the on-line sales server and the product evaluation server are implemented separately, the product evaluation server is characterized in that it works in conjunction with the on-line sales server consisting of at least one.

In addition, a product evaluation server device according to a second aspect of the present invention for achieving the above object, the interface unit is connected to the online sales server for selling goods on-line, the product information for the product and sell the product Collecting unit for collecting the activity of the customer's online shopping using a swap or web page to the storage, Storage unit for accumulating and managing the product information and the activity of the online shopping of the customer, Customer activity discrimination information forming unit for forming customer activity discrimination information based on the activity history for online shopping, the customer activity discrimination information when the customer evaluation information for the product is included in the activity history of the customer online shopping Confidence coefficient calculation unit for calculating an evaluation confidence coefficient for the customer evaluation information using The product evaluation information forming unit for forming the product evaluation information for the product, including the evaluation confidence coefficient and the customer evaluation information and for transmitting the product evaluation information to the online sales server to display on the swap or web page It characterized in that it comprises a transmission unit.

Preferably, when at least one of the product information and the activity history of the customer's online shopping is updated, the product evaluation server device updates the customer activity determination information, the evaluation confidence coefficient and the product evaluation information. The apparatus may further include an update processing unit for reforming.

Preferably, the activity history for the online shopping of the customer is characterized in that it comprises the history of the product information, the purchase history for the product and the formation of the customer evaluation information.

Preferably, the customer activity discrimination information forming unit, the number of clicks formed while viewing the product information that can be determined through the activity history for the online shopping of the customer, the purchase rate for the product compared to the number of clicks, The customer activity discrimination information may be formed by using at least one of a satisfaction rate for the customer evaluation information and a recommendation rate for the customer evaluation information as a parameter.

Preferably, the confidence coefficient calculation unit is a recommendation ratio formed through the recommendation or non-recommendation of the other customer for the customer evaluation information, the number of the customer evaluation information, the number of clicks formed while viewing the product information, and And multiplying the number of evaluations of other customers with respect to the customer evaluation information to calculate an evaluator support rate for the customer.

Preferably, the coefficient of confidence calculation unit is characterized by calculating the customer confidence coefficient for the formation of the customer evaluation information by multiplying the satisfaction index for the product of the customer and the rate of support of the evaluator.

Preferably, the confidence coefficient calculation unit forms a customer preference weight by multiplying the customer's product preference index and the customer's purchase history, and then multiplying the customer's evaluation propensity index by the customer's evaluation propensity index to form a customer propensity weight. The evaluation reliability coefficient may be calculated by multiplying the customer trust coefficient, the customer preference weight, the customer propensity weight, and the penalties included in the blacklist.

Preferably, the confidence coefficient calculation unit does not perform calculation on the evaluation confidence coefficient when each parameter for calculating the evaluation confidence coefficient does not reach a threshold of a predetermined level.

Preferably, the product evaluation information forming unit, in the case of not calculating the evaluation confidence coefficient, the customer evaluation information, the number of clicks on the customer evaluation information, the number of evaluations for the customer evaluation information and the evaluation of the customer evaluation information Forming the product evaluation information, including the average of the.

In addition, the product evaluation method in the on-line sales according to the third aspect of the present invention for achieving the above object, the interface step of the product evaluation server communicates with the on-line sales server for selling the goods online, for the product Collecting step for collecting the product information and the activity history for the customer's online shopping utilizing a swap or web page for selling the product, and accumulates and stores the product information and activity history for the customer's online shopping Store step of forming, the step of forming the customer activity identification information to form the customer activity identification information based on the activity history of the online shopping of the customer, the customer evaluation information of the product of the activity history of the online shopping is included If there is, the evaluation model for the customer evaluation information using the customer activity discrimination information A confidence factor calculating step for calculating a trust factor, a step of forming product evaluation information for forming product evaluation information for the product including the evaluation confidence coefficient and the customer evaluation information, and the product evaluation information in the online sales server It characterized in that it comprises a transmission step for transmitting to the swap or to appear on the web page.

Preferably, the product evaluation method in the on-line sales, if any one or more of the product information and the activity history of the online shopping of the customer is updated, the product evaluation server the customer activity identification information, the evaluation confidence coefficient And an update processing step of updating and remodeling the product evaluation information.

Preferably, the calculating of the confidence coefficient is characterized in that the calculation of the evaluation confidence coefficient is not performed when each parameter for calculating the evaluation confidence coefficient does not reach a threshold of a predetermined level.

Preferably, the step of forming the product evaluation information is in the case of the non-calculation of the evaluation reliability coefficient, the customer evaluation information, the number of clicks on the customer evaluation information, the number of evaluations on the customer evaluation information and the customer evaluation information It characterized in that to form the product evaluation information, including the average of the evaluation.

Therefore, in the present invention, by providing the customer's evaluation information on the products sold online by reforming and providing the unaltered, personalized information so that other customers can use them as purchase information on the products, the customer interested in a particular product It is an advantage to create an online shopping environment that can utilize other customers' evaluation information based on trust as information that can help them make purchasing decisions. Based on this online shopping environment, the evaluation of products in online shopping can be performed. Provide additional services (e.g., rewards for customers with high valuation trust levels or adjust discount rates at shopping malls according to levels) for business or online shopping provided as a professional service. There is an advantage to this.

Hereinafter, a preferred embodiment of a product evaluation system in on-line sales according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a product evaluation system in online sales according to an embodiment of the present invention. As shown by way of example only in Fig. 1, the product evaluation system in the online sales is a customer terminal device 100, a customer terminal device for enabling the customer to access the online shopping and to search for and purchase a desired product ( In response to a connection request of 100), an online sales server 200 which provides a web page or a swap for selling a product online, and the one or more products are viewed by the customer terminal device 100, Based on the activity of the online shopping of the customer provided after receiving the activity of the online shopping of the customer such as the purchase and the formation of customer evaluation information on the specific product input by the customer from the online sales server 200 By forming activity discrimination information, the evaluation confidence coefficient for customer evaluation information is calculated, and the evaluation confidence coefficient calculated in this way is evaluated. Reflecting in the beam includes a product evaluation server 300 for forming product evaluation information.

Here, the product evaluation server 300 transmits the formed product evaluation information to the online sales server 200, accordingly, the online sales server 200 displays the received product evaluation information in the corresponding session of the swap or web page screen .

The method of displaying product evaluation information in the online sales server 200 can be implemented in various ways, but for example, on one side of customer evaluation information (for example, in the form of a comment) for a specific product input from a customer. By displaying an evaluation confidence coefficient numerically or graphically, it is possible for other customers who come to know it to determine how reliable the evaluation is.

Before going into more detail, the customer's buying behavior is described by the AIDA Model. The customer recognizes that there is a specific product, and then induces interest in it, and afterwards, in case of a specific brand or a desired product, the customer feels a desire to purchase, and then proceeds to a purchase act.

In this regard, the customer leaves a trace of action at the stage of recognizing the product and showing interest.

For example, an example may be a case where an advertisement delivered by e-mail generates interest and a user clicks to go to a sales site. Another example is recognizing a product through a TV advertisement and searching and searching in an internet shopping mall. An example would be to see the plot of the VoD that could be of interest through the IPTV VoD history.

As such, the concept of a product in the present invention includes a product such as a bag, clothing and a notebook, or a service such as a broadcast content and a program for a specific function.

The customer wants to obtain information about the item online even when he / she wants to purchase the above-mentioned items such as bags, clothes and laptops through online shopping, or when he / she wants to purchase the goods through offline shopping.

Similarly, when a customer wants to purchase a service such as broadcast content and a program for a specific function through online shopping or offline shopping, as described above, the customer acquires prior information about the service and then uses the collected result. You will purchase the service you want at an affordable price.

Hereinafter, the present invention will be described in more detail by unifying the goods or services as 'goods'.

A customer who has accessed a product through these various possible channels would like to obtain information about the product in order to make a purchase decision. This information compares various facts about price, features, shipping and discounted prizes.

As part of this, additional information such as popularity, sales volume, and testimonials may be used as an important selling decision factor in an internet shopping mall.

In addition, in terms of popularity, it is difficult to designate it as a popular product because a customer clicks and browses a lot of products. This can be viewed as a popular product only after actually reading and judging the degree leading to the purchase.

Otherwise, sales volume and popularity become equivalent, meaning that popularity is meaningless. Information that the customer has seen a lot but the ratio that leads to the actual purchase is not high can be considered as important for the buyer.

In addition, the number of clicks may be divided into the number of clicks on the same product and the number of clicks on a product sold by a particular seller, and the purchase rate should also be used as a meaningful number.

After a customer purchases and uses a product online, the satisfaction rating can be very different. These satisfactions can be evaluated in various ways such as high / medium / low, satisfaction / normal / complaint, over expectation / expectation level / under expectation / complaint, 1 ~ 5, and also design, function, quality, price, delivery, It is also possible to evaluate each item broken down by service or AS.

Therefore, the above-mentioned activity of the customer's online shopping refers to the process of normalizing the number of clicks on a specific product in the online shopping, the number of recommendations for customer evaluation information written for the specific product, and the customer's disposition. It refers to the information scored through.

This normalization process is to evaluate customer evaluation information more objectively and systematically so that it can be used as important purchase decision information for other customers.

The items that should be reflected in the normalization process will vary according to the situation, price, etc. in online shopping, but if you look at some of them, the customer who was consistent with the general or conservative evaluation generally has a high level of If they were satisfied, they would be given a relatively high score.

In addition, if there are evaluators who are negatively evaluating the function of a specific product, if there is no record of other evaluations on the evaluation result or if the level is below a certain level, the evaluation result will be low.

In addition to the overall evaluation of a specific product, the evaluation can be broken down into subdivisions by subdividing the evaluation items.

Furthermore, in the case of an evaluator who has a lot of recommendation and support from other customers in the evaluation and review reflecting the confidence / support of the evaluator, the confidence / support level is relatively high.

This confidence is higher when there are more evaluators, for example, because the testimonials purchased by 500 people are more reliable than the testimonials purchased by 5 people.

In addition, the reliability can be changed according to the customer's buying behavior, that is, if the customer prefers the action material, the evaluation of the mellow water may not be good. Similarly, assessments of IT devices, especially those with similar product purchase history, should be rated as more reliable than those with low IT device purchase rates.

The product evaluation server 300 for normalizing the customer product information by reflecting the reliability as described above, the interface unit 310 is connected to the online sales server 200 for selling the product online, product information for the product And a collection unit 320 for collecting the activity history of the customer's online shopping, a storage unit 330 for accumulating and storing the collected product information and the activity history of the customer's online shopping, and the customer's online shopping. Customer activity discrimination information forming unit 360 that forms customer activity discrimination information based on the activity history, and uses customer activity discrimination information when the customer evaluation information on the product is included in the activity history of the customer online shopping. Confidence coefficient calculation unit, evaluation confidence coefficient, and customer evaluation information for calculating the evaluation confidence coefficient (ie, the above-mentioned reliability items) for evaluation information And a product evaluation information forming unit 360 for forming product evaluation information on the product, and a transmission unit 370 for transmitting the product evaluation information to the online sales server 200.

Subsequently, the online sales server 200 (eg, a web server corresponding to a web site for providing online shopping) receives the product evaluation information from the product evaluation server 300 and displays the information on the swap or web page screen. .

The product evaluation server 300, when any one or more of the product information and the activity history of the customer's online shopping is updated, the update processing unit 380 for updating and reforming the customer activity identification information, evaluation confidence coefficient and product evaluation information It is preferable to further include).

Customer activity discrimination information forming unit 360 is a purchase rate for the product compared to the number of clicks, clicks formed while viewing the product information based on the activity of the customer's online shopping provided from the online sales server 200 In this case, the customer activity discrimination information is formed using at least one of a satisfaction rate for the customer evaluation information and a recommendation rate for the customer evaluation information.

In addition, the confidence coefficient calculation unit executes the following process to calculate the evaluation confidence coefficient for the customer evaluation information formed by the customer's evaluation of the product.

First, by multiplying the recommendation rate formed through recommendation or deprecation of other customer evaluation information, the number of customer evaluation information, the number of clicks formed while viewing product information, and the number of other customer evaluations on customer evaluation information Calculate rating support for customers.

Then, the customer confidence coefficient for the formation of customer evaluation information is calculated by multiplying the satisfaction index and the evaluation support rate for the customer's product.

The customer preference weights are formed by multiplying the customer preference index by the customer's purchasing history, and then the customer propensity weight index is formed by multiplying the customer's evaluation propensity index and the overall customer's evaluation propensity index. If it is included in the preference weight, the customer propensity weight, and the blacklist, the evaluation confidence coefficient is calculated by multiplying the corresponding penalty.

Here, the customer preference weight is to assign weights as much as the corresponding proportion to the products preferred by the customer, as determined by the preference of the customer and the purchase history of the customer.

Also, unlike the customer preference weights, the customer propensity weights are different from the customer preference weights, and the evaluation propensity of the customers is proportional to the average appraisal tendency of the entire buyer (for example, the satisfaction rate or dissatisfaction rate for the product). It refers to the quantification of the discrimination result after the determination of the degree of intolerance.

In addition, the customer's evaluation propensity index is an index of the customer's evaluation propensity for a specific product. For example, a process for calculating the customer's evaluation propensity ratio, a recommendation ratio and a recommendation for a specific product while shopping online is recommended. The ratio can be used as basic information for setting the evaluation tendency index, and the recommendation or non-recommendation ratio for customer evaluation information inputted by other customers for a specific product can also be used as basic information for setting the evaluation tendency index. Do.

Similarly, the evaluation tendency index of all buyers may be calculated similarly to the method of calculating the evaluation tendency index of the customers.

On the other hand, it is preferable for the distortion-free evaluation to not perform the calculation of the evaluation reliability coefficient when each parameter for calculating the evaluation reliability coefficient does not reach a threshold of a predetermined level.

Accordingly, when the evaluation confidence coefficient is not calculated, the product evaluation information forming unit 360 calculates the customer evaluation information, the number of clicks on the customer evaluation information, the number of evaluations on the customer evaluation information, and the average of the evaluations on the customer evaluation information. Product evaluation information can be formed, including.

FIG. 2 is a flowchart illustrating an operation process of a product evaluation system in the online sale shown in FIG. 1. As shown by way of example only in Fig. 2, the product evaluation method in the online sales, after the customer terminal device 100 is connected to the online sales server 200 for online shopping via a wired or wireless network, the online sales The process proceeds to receiving a web page or a swap for selling the online goods provided from the server 200 (S100 and S102).

Afterwards, the customer purchases the product information on the specific product through the customer terminal device 100 and purchases the product, or enters the customer evaluation information in the form of a review for the specific product while online shopping. It leaves its own activities (S104).

After storing and storing the activity history related to the online shopping of the customer in step S104, and delivers it to the product evaluation server 300 (S106 and S108).

The product evaluation server 300 accumulates and stores the activity history for the online shopping of the customer, which is delivered from the online sales server 200 (S110).

Subsequently, after forming a customer activity discrimination information that scores the activity history of the stored customer's online shopping through a normalization process (S112), the evaluation of the customer evaluation information left by the specific customer is performed using the formed customer activity discrimination information. The confidence coefficient is calculated (S114).

The product evaluation server 300 forms product evaluation information for reflecting the calculated evaluation confidence coefficient in the corresponding customer evaluation information and transmits the product evaluation information to the online sales server 200 (S116 and S118).

Thus, the online sales server 200 executes to display the product evaluation information on the web page screen or a swap for online shopping (S120).

3 is a block diagram of a product evaluation system in online sales according to another embodiment of the present invention. As shown by way of example only in FIG. 3, a product valuation system in an online sale may include at least one online sales server 500 (eg, a first online sales server) that provides a swap or web page for selling products online. 500, the second online sales server 500 and the Nth online sales server 500, and at least one online sales server 500 to access a specific online shopping (eg, the first online shopping to the Nth online shopping). Customer terminal device 400, and the customer terminal device 400 to view any one or more of the product, the purchase of the product and the formation of customer evaluation information for the specific product input by the customer After the customer's online shopping activity is provided from the online sales server 500, the customer activity discrimination information is formed based on the activity of the online shopping. W comprises a product evaluation server 600 for forming a product evaluation information, as it calculates the evaluated confidence factor for the rating information from, and reflect the evaluation confidence coefficients thus calculated to the rating information.

Here, the product evaluation server 600 forms a communication connection with at least one online sales server 500, and then provides product evaluation information for the corresponding product for each online shopping corresponding to the at least one online sales server 500. According to this, it can be a general configuration that can implement a business that can professionally perform the evaluation for online shopping.

The product evaluation server 600 delivers the formed product evaluation information to the online sales server 500. Accordingly, the online sales server 500 displays the received product evaluation information in the corresponding session of the swap or web page screen.

The method for displaying product evaluation information in the online sales server 500 can be implemented in various ways. For example, on one side of customer evaluation information (for example, in the form of a comment) for a specific product input from a customer. By displaying an evaluation confidence coefficient numerically or graphically, it is possible for other customers who come to know it to determine how reliable the evaluation is.

In addition, the product evaluation server 600 for normalizing the customer product information by reflecting the reliability described above, the interface unit 610 is connected to communicate with at least one or more online sales server 500 for selling products online A collection unit 620 for collecting product information on at least one product for each online sales server 500 and activity information on the online shopping of the customer, accumulated product information, and activity history on the online shopping of the customer Storage unit 630 for storing and storing, customer activity discrimination information forming unit 640 for forming customer activity discrimination information based on the activity history of the customer's online shopping, customer for the product of the activity history for the customer's online shopping In the case where the evaluation information is included, the evaluation reliability coefficient (ie, the reliability item mentioned above) for the customer evaluation information is determined using the customer activity discrimination information. The product evaluation information forming unit for forming the product evaluation information for the product, including the confidence coefficient calculation unit 650, the evaluation reliability coefficient, and the customer evaluation information, and the product evaluation information for calculating the product evaluation information. And a transmitting unit 670 for transmitting to 500.

Thereafter, the at least one online sales server 500 (eg, a web server corresponding to a web site for providing online shopping) receives the product evaluation information from the product evaluation server 600 and displays it on a swap or web page screen. Run it forever.

The product evaluation server 600, when one or more of the product information and the activity history of the customer's online shopping is updated, the update processing unit for updating and reforming the customer activity discrimination information, evaluation confidence coefficient and product evaluation information 680 It is preferable to further include).

The customer activity discrimination information forming unit 640 purchases a ratio of the product compared to the number of clicks and clicks formed while viewing the product information based on the activity of the customer's online shopping provided from the online sales server 500. In this case, the customer activity discrimination information is formed using at least one of a satisfaction rate for the customer evaluation information and a recommendation rate for the customer evaluation information.

In addition, the confidence coefficient calculation unit 650 executes the following process to calculate the evaluation confidence coefficient for the customer evaluation information formed by the customer's evaluation of the product.

First, by multiplying the recommendation rate formed through recommendation or deprecation of other customer evaluation information, the number of customer evaluation information, the number of clicks formed while viewing product information, and the number of other customer evaluations on customer evaluation information Calculate rating support for customers.

Then, the customer confidence coefficient for the formation of customer evaluation information is calculated by multiplying the satisfaction index and the evaluation support rate for the customer's product.

The customer preference weights are formed by multiplying the customer preference index by the customer's purchasing history, and then the customer propensity weight index is formed by multiplying the customer's evaluation propensity index and the overall customer's evaluation propensity index. If it is included in the preference weight, the customer propensity weight, and the blacklist, the evaluation confidence coefficient is calculated by multiplying the corresponding penalty.

On the other hand, the confidence coefficient calculation unit 650 preferably does not perform the calculation of the evaluation reliability coefficient when each parameter for calculating the evaluation reliability coefficient does not reach a threshold of a predetermined level.

Accordingly, when the evaluation confidence coefficient is not calculated, the product evaluation information forming unit includes the product evaluation information, the number of clicks on the customer evaluation information, the number of evaluations on the customer evaluation information, and the average of the evaluations on the customer evaluation information. Evaluation information can be formed.

4 is a flowchart illustrating an operation process of a product evaluation system in the on-line sales shown in FIG. 3. As shown by way of example only in Fig. 4, the product evaluation method in the online sales is a case where the customer terminal device 400 performs the first online shopping of the first online shopping or the N-th online shopping through a wired network or a wireless network. After connecting to the first online sales server 500 corresponding to this, proceeds to receive a web page or a swap for selling the online goods provided from the first online sales server 500 (S200 and S202).

Thereafter, the customer views the product information on a specific product through the customer terminal device 400 and purchases the product, or inputs customer evaluation information in the form of a review for the specific product. While leaving the activities of their own (S204).

After storing and storing the activity history related to the first online shopping of the customer in step S204, it is transmitted to the product evaluation server 600 (S206 and S208).

The product evaluation server 600 receives the activity information related to the first online shopping of the customer as data information, and then determines whether the management service is evaluated by determining the sender who transferred the data (S210).

If it is determined in step S210 that the first online shopping, and activates the product evaluation service for the first online shopping (S212).

Thereafter, the product evaluation server 600 accumulates and stores the activity history for the online shopping of the customer, which is transmitted from the online sales server 500 (S214).

Subsequently, after forming the customer activity discrimination information that scores the activity history of the stored customer's first online shopping through a normalization process (S216), the customer activity identification information left by the specific customer is formed using the formed customer activity discrimination information. Compute the evaluation reliability coefficient (S218).

The product evaluation server 600 forms product evaluation information for reflecting the calculated evaluation confidence coefficient in the corresponding customer evaluation information and transmits the product evaluation information to the online sales server 500 (S220 and S222).

Accordingly, the online sales server 500 executes to display the product evaluation information on the swap or web page screen for online shopping (S224).

Although the above has been described with reference to a preferred embodiment of the present invention, those skilled in the art will be variously modified and changed within the scope of the invention without departing from the spirit and scope of the invention described in the claims below. I can understand that you can.

In addition, the present invention is intended to reformate and provide customers' evaluation information on products sold online as unmodified and personalized information so that other customers can use them as purchase information on products, It is an invention with industrial applicability because it is not only sufficient but also practically evident.

1 is a block diagram of a product evaluation system in online sales according to an embodiment of the present invention,

2 is a flowchart illustrating an operation process of a product evaluation system in the on-line sales shown in FIG. 1;

3 is a block diagram of a product evaluation system in online sales according to another embodiment of the present invention, and

4 is a flowchart illustrating an operation process of a product evaluation system in the on-line sales shown in FIG. 3.

<Description of Symbols for Main Parts of Drawings>

100, 400: customer terminal device 200, 500: online sales server

300, 600: product evaluation server 310, 610: interface unit

320, 620: collection unit 330, 630: storage unit

340, 640: Customer activity discrimination information forming unit 350, 650: Trust coefficient calculation unit

360, 660: product evaluation information forming unit 370, 670: transmission unit

380, 680: update processing unit

Claims (16)

An online sales server providing product information on the product through a web page or a swap for selling the product online; A customer terminal device for supporting a customer's online shopping, including viewing the product information, purchasing the product, and forming customer evaluation information; After forming the customer activity identification information based on the activity of the customer's online shopping, calculate the confidence coefficient for the customer evaluation information through the customer activity identification information, the product including the confidence coefficient and the customer evaluation information And a product evaluation server configured to display the product evaluation information on the swap or web page as the evaluation information is formed and transmitted to the online sale server. The method of claim 1, The product evaluation system in the online sale may be implemented by a server integrating the online sales server and the product evaluation server or by separately implementing the online sales server and the product evaluation server. system. The method according to claim 1 or 2, If the online sales server and the product evaluation server is implemented separately, the product evaluation system in the on-line sales, characterized in that the product evaluation server is interlocked with the at least one online sales server. An interface unit communicating with an online sales server for selling goods online; A collecting unit for collecting activity information on online shopping of a customer using product information on the product and a web page or a swap for selling the product; A storage unit for accumulating and managing the product information and the activity history of the online shopping of the customer; A customer activity discrimination information forming unit for forming customer activity discrimination information based on the activity history of the customer online shopping; A confidence factor calculation unit for calculating an evaluation confidence coefficient for the customer evaluation information using the customer activity identification information when the customer evaluation information on the product is included in the activity of the customer online shopping; A product evaluation information forming unit for forming product evaluation information on the product including the evaluation confidence coefficient and the customer evaluation information; And And a transmission unit for transmitting the product evaluation information to the online sales server so as to be displayed on the swap or web page. The method of claim 4, wherein The product evaluation server device may be configured to update and reconstruct the customer activity discrimination information, the evaluation confidence coefficient, and the product evaluation information when at least one of the product information and the activity history of the customer online shopping is updated. An update processing unit; Product evaluation server device further comprising. The method according to claim 4 or 5, The activity history for the online shopping of the customer is a product evaluation server device, characterized in that it comprises a reading history for the product information, the purchase history for the product and the formation of the customer evaluation information. The method according to claim 4 or 5, The customer activity discrimination information forming unit may include any one of a number of clicks formed while viewing the product information, a purchase rate for the product compared to the number of clicks, a satisfaction rate for the customer evaluation information, and a recommendation rate for the customer evaluation information. The product evaluation server device, characterized in that for forming the customer activity discrimination information using at least one parameter. The method according to claim 4 or 5, The confidence coefficient calculation unit may include a recommendation ratio formed through recommendation or non-recommendation of another customer with respect to the customer evaluation information, the number of the customer evaluation information, the number of clicks formed while viewing the product information, and the customer evaluation information. And multiplying the number of evaluations of other customers with respect to the product evaluation server. The method of claim 8, And the confidence coefficient calculation unit calculates a customer confidence coefficient for forming the customer evaluation information by multiplying the satisfaction index for the product by the customer and the rate of support of the evaluator. The method according to claim 4 or 5, The confidence coefficient calculation unit multiplies the product preference index of the customer by the purchase history of the customer to form a customer preference weight, and then multiplies the evaluation propensity index of the customer by the evaluation propensity index of the entire customer to form a customer propensity weight. The evaluation confidence coefficient by multiplying the customer confidence coefficient, the customer preference weight, the customer preference weight, and the penalties included in the blacklist by forming the customer evaluation information formed by multiplying the satisfaction index for the product and the rate of support of the evaluator Product evaluation server device, characterized in that for calculating. The method according to claim 4 or 5, And the confidence coefficient calculation unit does not execute the calculation of the evaluation confidence coefficient when each parameter for calculating the evaluation confidence coefficient does not reach a threshold of a predetermined level. The method according to claim 4 or 5, The product evaluation information forming unit includes the customer evaluation information, the number of clicks on the customer evaluation information, the number of evaluations on the customer evaluation information, and the average of the evaluations on the customer evaluation information in case of no calculation of the evaluation confidence coefficient. The product evaluation server device, characterized in that to form the product evaluation information. An interface step of the product evaluation server communicating with an online sales server for selling the products online; A collecting step of collecting activity information on the online shopping of the customer utilizing the product information on the product and a swap or web page for selling the product; A storage step of accumulating and managing the product information and the activity history of the online shopping of the customer; A step of forming customer activity discrimination information for forming customer activity discrimination information based on the activity history of the customer's online shopping; A calculation of a confidence coefficient for calculating an evaluation confidence coefficient for the customer evaluation information using the customer activity identification information when the customer evaluation information on the product is included in the activity of the customer online shopping; Forming product evaluation information for forming product evaluation information on the product including the evaluation confidence coefficient and the customer evaluation information; And And a transmission step of transmitting the product evaluation information to the online sales server so that the product evaluation information is displayed on the swap or web page. The method of claim 13, The product evaluation method in the on-line sales, if any one or more of the product information and the activity history of the online shopping of the customer is updated, the product evaluation server the customer activity discrimination information, the evaluation confidence coefficient and the product evaluation And an update processing step for updating and reforming the information. The method according to claim 13 or 14, In the calculating of the confidence coefficient, the method for evaluating the product in the online sales, characterized in that the evaluation confidence coefficient is not calculated when each parameter for calculating the evaluation confidence coefficient does not reach a threshold of a predetermined level. . The method according to claim 13 or 14, In the step of forming the product evaluation information, in the case of not calculating the evaluation reliability coefficient, the average of the customer evaluation information, the number of clicks on the customer evaluation information, the number of evaluations on the customer evaluation information, and the customer evaluation information The product evaluation method in the online sale, characterized in that to form the product evaluation information including.

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