KR20110054881A - System and method for recommending credit card - Google Patents

Abstract

PURPOSE: A credit card recommendation system and method thereof are provided to recommend credit cards according to the taste of a user by using the weights according to credit card attributes. CONSTITUTION: A credit card attribute database(141) stores the attribute of each credit card. A credit card weight database(142) stores weights information according to each credit card reference attribute. A user recommendation credit card searcher(13) selects a plurality of credit card attributes from credit card attribute database. The user recommendation credit card searcher calculates weights according to user credit card attribute.

Description

System and method for recommending credit card}

The present invention relates to a credit card recommendation system and method, and more particularly, to calculate a weight for each credit card attribute based on a user based on a quantum comparison of each of the credit card attributes selected by a user, and to use the credit card ranking. The present invention relates to a system and method for recommending a credit card desired by a user by calculating.

Recently, as the use of credit cards has become more active, several types of credit card products have been released. There are credit card products with various benefits, as well as credit card products with only one benefit.

The user who wants to issue a credit card has a problem that it is not easy to determine which credit card is a good card when various types of credit cards are provided and their benefits are also various.

In addition, the existing method of recommending a credit card is to recommend a credit card that provides the most benefit of the selected one when the user selects one of a plurality of views. Nevertheless, there was a problem in that the comparison with only one benefit and the recommendation criteria were vague in recommending credit cards that provide various benefits.

The present invention calculates the weight for each credit card attribute based on the user through quantum comparison of each of the plurality of credit card attributes selected by the user, and calculates the ranking for each credit card using the same to recommend the credit card according to the user's preference. It is an object to provide a system and method.

Credit card recommendation system according to the present invention, the user terminal that can be connected to the Internet; A credit card attribute database that stores attributes of a credit card that are benefits of a credit card; A credit card weight database that stores information on weights for each credit card reference attribute; When a user terminal is connected through the Internet, a plurality of credit card attributes are selected from the credit card attribute database, a weighted value for each user-based credit card attribute is calculated for the selected credit card attributes, and the calculated user-based credit card attribute And a user recommendation credit card search unit for searching for a desired credit card by combining the weight of each star with the weight for each credit card reference attribute stored in the credit card weight database.

In the present invention, the user recommended credit card search unit, a user interface unit for providing a basic search menu and a search result screen to the user terminal; A credit card attribute selection unit for selecting a plurality of credit card attributes of interest to the user from the credit card attribute database and adding or deleting credit card attributes to the credit card attribute database according to external input; The credit card attribute selection unit compares each of the plurality of credit card attributes selected by each user, inputs the relative importance of the user criteria for each credit card attribute, and uses the user based credit by the user's intention through the input user relative relative importance for each credit card attribute. A weight calculation unit for each user credit card attribute that calculates a weight for each card attribute; According to an external input, the credit card weight database may change the weight for each credit card criterion attribute, and the weight for each credit card criterion attribute is multiplied by the weight for each credit card criterion attribute. By arranging the credit card recommendation unit for recommending a credit card according to the user's preferences.

In the present invention, the user recommended credit card search unit inputs the reciprocal of the user-based relative importance for each credit card attribute derived by comparing the plurality of credit card attributes selected by the credit card attribute selector, respectively, A weight calculation unit for each supplementary credit card attribute that calculates a weight for each supplementary credit card attribute for a supplementary credit card recommendation through an inverse of the relative importance of the user criteria for each card attribute; Complementary recommendation of at least one credit card that can complement the credit card according to the user's preferences by multiplying the weight of each complementary credit card attribute by the weight of the credit card reference attribute and arranging the credit cards in the order of high result value. Credit card recommendation unit; may further include.

In the present invention, the user-recommended credit card search unit analyzes whether the relative importance of the user criteria for each credit card attribute input by the user is compared with each other by comparing the plurality of credit card attributes, and if the user criteria are not consistent, It may further include a consistency analysis unit for requesting re-entry.

A credit card recommendation method according to the present invention comprises the steps of selecting a plurality of credit card attributes of interest to a user through a user terminal connected via the Internet; Inputting a user criterion relative importance for each credit card attribute by comparing the selected plurality of credit card attributes with each other; Calculating weights of user-based credit card attributes by a user's intention using the inputted user-based relative importance of each credit card attribute; Providing credit card information according to a user's preference by using the calculated user-based credit card attribute weights.

In the present invention, after the step of providing credit card information according to the user preferences, calculating the weight for each complementary credit card attribute for the recommendation of a complementary credit card using the inverse of the relative importance of the user criteria for each credit card attribute ; The method may further include providing supplementary credit card information for supplementing a credit card according to a user's preference by using the calculated weight for each supplementary credit card attribute.

In the present invention, after the step of inputting the relative importance of the user criteria for each credit card attribute, a plurality of credit card attributes selected by the user are compared with each other to analyze whether the relative importance of the user criteria for each credit card attribute is consistent. And inconsistency analysis step of requesting re-entry of the user if it is inconsistent.

In the present invention, the providing of the credit card information may include: multiplying the weight of each credit card attribute by the weight of the attribute of each credit card by the weight of each credit card attribute of the user criterion. It is characterized by arranging credit cards.

In the present invention, the weight for each user-based credit card attribute is calculated by Equation 1.

[Equation 1]

According to the present invention, the weight of each credit card attribute selected by the user is calculated by quantum comparison of each of the plurality of credit card attributes selected by the user, and the ranking of each credit card is calculated using the same to obtain the most desired credit card attributes. There is an effect that it can provide a credit card having.

As the invention allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description. However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all transformations, equivalents, and substitutes included in the spirit and scope of the present invention. In the following description of the present invention, if it is determined that the detailed description of the related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The terms are only used to distinguish one component from another.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

Hereinafter, an embodiment of a credit card recommendation system and method according to the present invention will be described in detail with reference to the accompanying drawings, and in the following description with reference to the accompanying drawings, the same or corresponding components are given the same reference numerals. Duplicate explanations will be omitted.

The term 'credit card attribute' used in the present invention refers to various benefits of credit cards such as shopping discount, gasoline discount, annual fee, etc., and the term 'criteria based on user's credit card attribute' used in the present invention The user thinks about each credit card attribute that is a benefit, and the term 'relative importance of user criteria for each credit card attribute' used in the present invention means that the user can compare each credit card attribute that is a benefit of the credit card. Considering the relative importance, the term 'weight per user credit card attribute' used in the present invention is a weight for each credit card attribute calculated by the user's relative importance for each credit card attribute input according to the user's intention, and the present invention The term 'weight by credit card attribute' is used in all credit card companies. Is a value quantified by measuring the relative importance of the attributes of a credit card and shopping discounts, discounted tour, annual fee for the card, respectively.

1 is a view showing a credit card recommendation system according to an embodiment of the present invention.

Referring to FIG. 1, a credit card recommendation system according to an embodiment of the present invention may include a user terminal 11, a user recommendation credit card search unit 13, and a database unit 14.

The user terminal 11 may be connected to the user recommended credit card search unit 13 through the Internet 12.

The database unit 14 includes a credit card attribute database 141 for storing attributes of credit cards, which are benefits of the credit card, and a credit card weight database 142 for storing information on weights for each credit card reference attribute. It may include.

The user recommendation credit card search unit 13 selects a plurality of credit card attributes from the credit card attribute database 141 when the user terminal 11 is connected through the Internet 12, and selects the selected credit card attributes. The user-based credit card attribute weights are calculated, and the calculated user-based credit card attribute weights are combined with the credit card-based attribute weights stored in the credit card weight database 142 to calculate the ranking by credit card. You can search your credit card.

The user recommendation credit card search unit 13 may include a user interface unit 131, a credit card attribute selection unit 132, a weight calculation unit 133 for each user-based credit card attribute, and a credit card recommendation unit 134. Can be.

The user interface 131 may provide a basic search menu and a search result screen to the user terminal 11.

The credit card attribute selector 132 may select a plurality of credit card attributes of interest to the user from the credit card attribute database 141, and apply the credit card attributes to the credit card attribute database 141 according to external input. You can add or delete it.

That is, the credit card attribute selector 132 adds a new credit card attribute to the credit card attribute database 141 which has not been reflected as a credit card having various benefits is released, or which the user no longer selects. If the card attribute can be removed from the credit card attribute database 141 and the user does not select the desired credit card attribute, then all credit card attributes stored in the credit card attribute database 141 are treated as selected. You can proceed to the next step.

The weight calculation unit 133 for each user-based credit card attribute compares each of the plurality of credit card attributes selected by the credit card attribute selector 132 and inputs the relative importance of the user-based criteria for each credit card attribute. Based on the relative importance of each user, a weight for each attribute of a credit card based on a user's intention can be calculated.

The credit card recommendation unit 134 may change the weight for each credit card criterion attribute in the credit card weight database 142 according to an external input, and multiply the weight for each credit card criterion attribute by the weight for each credit card criterion attribute. By arranging credit cards in the order of high result, the credit card ranking can be calculated to recommend credit cards according to user's preference.

That is, the credit card recommendation unit 134 may immediately reflect the change in the credit card weight database 142 when the credit card attribute, which is the benefits of the credit card of each card company, is changed.

In addition, the user recommendation credit card search unit 13 inputs the reciprocal of the user-based relative importance for each credit card attribute derived by comparing the plurality of credit card attributes selected by the credit card attribute selector 132, respectively, User standard by credit card attribute Based on the reciprocal of relative importance, complementary credit card attribute weight calculation unit (not shown) that calculates the weight of complementary credit card attributes for recommendation of complementary credit card Complementary credit card recommender recommending at least one credit card that can complement the credit card according to the user's preferences by multiplying the weight by attribute and arranging the credit cards in the order of high result. Not shown) may be further included.

That is, to calculate at least one weighted credit card attribute for each user-based credit card attribute to recommend at least one complementary credit card that can supplement the recommended credit card according to the user's preference. .

In addition, the user recommendation credit card search unit 13 compares each of a plurality of credit card attributes, and analyzes whether the relative importance of the user criteria for each credit card attribute input is consistent. The apparatus may further include a correspondence analyzer (not shown) requesting an input.

In order to recommend a credit card according to a user's preference, a quantum comparison of each of the user's selected credit card attributes must be consistent, so that it can be checked.

2 is an operation flowchart of a credit card recommendation system according to an embodiment of the present invention.

Referring to FIG. 2, first, a user may access a credit card recommendation system through the Internet 12 (S11). After accessing the credit card recommendation system, the user may select a plurality of credit card attributes of interest to the user through the user terminal 11 connected through the Internet 12 (S12). In this case, if the user does not select a credit card attribute, the user may proceed to the next step by treating all attributes stored in the credit card attribute database 141 as being selected. The user may compare each of the selected plurality of credit card attributes and input a relative criterion of importance based on each credit card attribute (S13). Subsequently, the credit card recommendation system calculates a weight for each user-based credit card attribute by the user's intention by using the input relative importance of the user-based credit card attribute (S14), and uses the calculated weight for each user-based credit card attribute. By calculating the ranking for each credit card can provide credit card information according to the user's preferences (S15). If the user selects at least one of the provided credit card information to be issued, the user automatically accesses the credit card site. If the user does not have the desired credit card among the provided credit card information, the plurality of credit card attributes of interest are provided. In step S16, the process may move back to the step where the user can select a step.

In addition, after the step of providing credit card information according to the user preferences (S15), calculating the weight for each complementary credit card attribute for the recommendation of the supplementary credit card using the inverse of the relative importance of the user criteria for each credit card attribute ( The method may further include providing supplementary credit card information for supplementing a credit card according to a user's preference using the calculated weight for each supplementary credit card attribute (not shown).

In addition, after the step of inputting the user-based relative importance for each credit card attribute (S13), a plurality of credit card attributes selected by the user are compared with each other to analyze whether the user-based relative importance for each credit card attribute is consistent. In case of inconsistency, the method may further include a consistency analysis step (not shown) for requesting re-entry of the user.

Hereinafter, a credit card recommendation system and method according to an embodiment of the present invention will be described with specific examples.

3 is a diagram illustrating quantum comparison by credit card attributes, and FIG. 4 is an exemplary view of quantum comparison by credit card attributes and calculating weights by user-based credit card attributes.

Referring to Figure 3, credit card attributes that are the benefits of credit cards A1, A2,. , An, and user-critical importance of each credit card attribute is set to V1, V2,... , And Vn, it is possible to calculate the relative importance of the user criteria for each credit card attribute by comparing each of the credit card attributes.

The user-based relative importance of each credit card attribute calculated by quantum comparison of each credit card attribute may be represented by a square matrix. Here, V1 / V1 = V11 is a relative importance value of A1 compared to A1, V1 / V2 = V12 is a relative importance value of A1 compared to An, and V1 / Vn = V1n is compared to An. The relative importance value of A1.

Using the relative importance of user criteria for each credit card attribute represented by a square matrix, the following weights (W1, W2, ..., Wn) for each user-based credit card attribute may be calculated through Equation 1 below.

[Equation 1]

For a more detailed description, referring to FIG. 4, a case in which a user selects a shopping discount, a fueling discount, and an annual fee as a credit card attribute of interest is one of the credit card attributes.

First, for comparison between the selected credit card attributes, the relative importance of each credit card attribute may be calculated and displayed in a square matrix by comparing shopping discounts and fueling discounts, shopping discounts and annual fees, fueling discounts and annual fees.

Looking at the calculated square matrix, it can be seen that the shopping discount is twice as important as the fueling discount and nine times more than the annual fee. In addition, it can be seen that the gasoline discount is 1/2 times more important than the shopping discount and 7 times more important than the annual fee. In addition, it can be seen that the annual fee is 1/9 times more important than the shopping discount and 1/7 times more important than the fueling discount.

When the square matrix derived by calculating the relative importance of the user criteria for each credit card attribute is substituted into [Equation 1], the weight for each user criteria credit card attribute can be calculated as shown in FIG. 4.

As shown in FIG. 4, the user-based shopping discount weight Ws is 0.598, the user-based fueling discount weight Wo is 0.346, and the user-based annual discount weight weight Wy is 0.056.

In this case, the weight of each credit card reference attribute stored in the credit card weight database 142 will be described as follows.

First, the weight of the credit card A for the shopping discount is Sa, the weight of the B credit card is Sb, and the weight of the C credit card is Sc (at this time Sa + Sb + Sc = 1). The weight of Oa and B credit card is the weight of Ob and C credit card is Oc (Oa + Ob + Oc = 1), and the weight of A credit card is the weight of Ya and B credit card for annual fee. Is Yb and the weight of the C credit card is Yc (Ya + Yb + Yc = 1).

 Here, the weight value for each credit card attribute (Ws, Wo, Wy) is multiplied by the weight for each credit card attribute to represent the result for each credit card. The value of A credit card is Ws * Sa + Wo * Oa + Wy * Ya, the value of B credit card is Ws * Sb + Wo * Ob + Wy * Yb, and the value of C credit card is Ws * Sc + Wo * Oc + Wy * Yc. The credit card having the highest value among these values may be arranged in order of maximum value so that the credit card according to the user's preference can be determined.

Thus, the user can issue the card having the maximum value among the arranged credit cards so that the user can obtain the desired credit card.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the appended claims. It will be understood that the invention may be varied and varied without departing from the scope of the invention.

Many embodiments other than the above-described embodiments are within the scope of the claims of the present invention.

1 is a view showing a credit card recommendation system according to an embodiment of the present invention.

2 is an operation flowchart of a credit card recommendation system according to an embodiment of the present invention.

3 is a diagram illustrating quantum comparison of credit card attributes.

4 is an exemplary diagram of quantum comparison between credit card attributes and calculation of weight for each credit card attribute based on a user.

<Description of the symbols for the main parts of the drawings>

11: user terminal

12: Internet

13: user recommended credit card search

14: database part

131: user interface unit

132: credit card attribute selection unit

133: weight calculation unit for each user credit card attribute

134: credit card recommendation

141: Credit card attribute database

142: Credit card weight database

Claims (10)

A user terminal capable of accessing the Internet; A credit card attribute database that stores attributes of a credit card that are benefits of a credit card; A credit card weight database that stores information on weights for each credit card reference attribute; When a user terminal is connected through the Internet, a plurality of credit card attributes are selected from the credit card attribute database, a weighted value for each user-based credit card attribute is calculated for the selected credit card attributes, and the calculated user-based credit card attribute And a user recommendation credit card search unit which searches for a desired credit card by combining the weight of each star with the weight of each credit card reference attribute stored in the credit card weight database. The method of claim 1, The user recommended credit card search unit, A user interface unit providing a basic search menu and a search result screen to the user terminal; A credit card attribute selection unit for selecting a plurality of credit card attributes of interest to the user from the credit card attribute database and adding or deleting credit card attributes to the credit card attribute database according to external input; The credit card attribute selection unit compares each of the plurality of credit card attributes selected by each user, inputs the relative importance of the user criteria for each credit card attribute, and uses the user based credit by the user's intention through the input user relative relative importance for each credit card attribute. A weight calculation unit for each user credit card attribute that calculates a weight for each card attribute; According to an external input, the credit card weight database may change the weight for each credit card criterion attribute, and the weight for each credit card criterion attribute is multiplied by the weight for each credit card criterion attribute. Credit card recommendation system comprising; credit card recommendation unit for recommending a credit card according to the user's preferences. The method of claim 2, The user recommended credit card search unit, Input the reciprocal of the user-based relative importance for each credit card attribute derived by comparing the plurality of credit card attributes selected by the credit card attribute selection unit, respectively, and complement the credit through the reciprocal of the user-based relative importance for each credit card attribute. A weight calculation unit for each supplementary credit card attribute for calculating a weight for each supplementary credit card attribute for card recommendation; Complementary recommendation of at least one credit card that can complement the credit card according to the user's preferences by multiplying the weight of each complementary credit card attribute by the weight of the credit card reference attribute and arranging the credit cards in the order of high result value. Credit card recommendation system; further comprising a credit card recommendation system. The method of claim 2, The user recommended credit card search unit, It further includes a matching analysis unit for analyzing whether the relative importance of the user criterion for each credit card attribute entered by the user by comparing a plurality of credit card attributes, respectively, and requesting re-entry of the user if the consistency is not consistent; Credit card recommendation system. The method according to any one of claims 1 to 4, The user-based credit card attribute weights (W1, W2, ..., Wn) is calculated by Equation 1 characterized in that the credit card. [Equation 1]

On the other hand, V1n, V2n,... , Vnn is the relative importance of the user criteria by credit card attribute. Selecting a plurality of credit card attributes of interest to the user through a user terminal connected through the Internet; Inputting a user criterion relative importance for each credit card attribute by comparing the selected plurality of credit card attributes with each other; Calculating weights of user-based credit card attributes by a user's intention using the inputted user-based relative importance of each credit card attribute; And providing credit card information according to a user's preference by using the calculated user-based credit card weights for each attribute. The method of claim 6, After the step of providing credit card information according to the user preferences, Calculating weights for each supplementary credit card attribute for recommending a supplementary credit card using a reciprocal of a user criterion relative importance for each credit card attribute; And providing supplementary credit card information that supplements the credit card according to the user's preference using the calculated weight for each supplementary credit card attribute. The method of claim 6, After the step of inputting the relative importance of the user criteria for each credit card attribute, the plurality of credit card attributes selected by the user are compared with each other to analyze whether or not the relative importance of the user criteria for each credit card attribute is consistent. If the user re-enter a match analysis step; further comprising a credit card recommendation method. The method of claim 6, Providing the credit card information, The credit card recommendation method of claim 1, wherein the weight of each attribute of the credit card is multiplied by the weight of the attributes of each credit card. The method according to any one of claims 6 to 9, The weight criteria (W1, W2, ..., Wn) for each user-based credit card attribute are calculated by Equation (1). [Equation 1]

On the other hand, V1n, V2n,... , Vnn is the relative importance of the user criteria by credit card attribute.

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