KR101600893B1 - Product satisfaction inference system based on fuzzy integral considering subjective decision-making tendencies - Google Patents

Abstract

The present invention relates to a fuzzy integral-based product satisfaction inference system that takes into consideration individual subjective propensity of a user, more specifically, to infer the relative importance of a user's preference in an Analytic Hierarchy Process (AHP) And the evaluation value of the degree of satisfaction with the product is calculated by applying the fuzzy integral so that it is possible to infer the satisfaction degree of the product reflecting the subjective inclination of the user and furthermore, And a system in which a product reflecting a tendency can be recommended.

Description

{PRODUCT SATISFACTION INFERENCE SYSTEM BASED ON FUZZY INTEGRAL CONSIDERING SUBJECTIVE DECISION-MAKING TENDENCIES}

The present invention relates to a fuzzy integral-based product satisfaction inference system that takes into consideration subjective propensity of a user.

More specifically, in order to infer product satisfaction in consideration of user preference, the relative importance of user's preference is calculated using the Analytic Hierarchy Process (AHP) technique, and the evaluation value of product satisfaction is calculated by Fuzzy Integral The present invention relates to a system that can deduce a product satisfaction that reflects a user's subjective inclination and can recommend a product that reflects a user's subjective inclination according to an inferred product satisfaction.

In recent years, social commerce, which is a new type of commerce, is growing rapidly. Especially, when social networking services (SNS) are widely spread, more than a certain number of buyers are collecting social commerce.

Since the types of goods belonging to the similar category or the same search category are so large, it is difficult for consumers to select the products considering the price and the discount rate in a comprehensive manner. Instead, as the number and types of the products are diversified, There is a problem in that it is not possible to select a product reflecting a subjective opinion.

That is, in order to select a product in which the subjective inclination of the consumer is reflected among the products having a wide range of kinds, it is necessary to deduce the product satisfaction according to the predetermined criterion and to recommend the product.

As a related art, Japanese Patent Laid-Open No. 10-2002-0038446 discloses a system and method for searching for goods information based on product satisfaction on the Internet.

The technology described above has a purpose of reducing the effort and time required for finding a product meeting the purchasing condition of the purchaser. It is a technique of storing the product information database storing the product information inputted by the seller and the purchase condition inputted by the buyer The stored purchase condition database is compared with each other, information of the satisfaction of the goods is calculated, and the goods information can be retrieved and provided to the user.

However, the technology described above merely compares the purchase information of the purchaser with the merchandise information of the seller so that an appropriate merchandise is searched, and when the purchaser inputs purchase information to retrieve and provide the merchandise information, The ambiguity of the generated language can not be solved, so that it can not be said that the product information reflecting the subjective tendency of the purchaser is received.

For example, in the case of inputting purchase information, if the buyer is assumed to be able to select the elements for A and B, the buyer's choice is human behavior, so he chooses "definitely" or "surely B" It is impossible, for example, even if you choose 'A', there is an uncertain ambiguity about the importance of 'B'.

In other words, the above-described technique can not solve the problem that the consumer can not select a product reflecting his / her subjective intention on the social commerce having a variety of products and kinds of products.

On the other hand, as a technique for solving the above problem, the rule-based insurance product recommendation and design system implementation through the fuzzy inference is published in the Korean Journal of Electronic Commerce, which sets rules through fuzzy inference to provide insurance products And calculate the relative importance of the evaluation items through the Delphi technique.

Professor Thomas Sati of the United States who opened the AHP technique explained that the layered technique such as AHP is better suited for the human recognition type compared with the AHP technique and the Delphi technique. (POSCO Management Research Institute, Chung Yoon, 2010. 06)).

Accordingly, the inventor of the present invention has developed a system capable of deducing the product satisfaction of the consumer by applying the AHP technique and the fuzzy theory, and recommending the product according to the inferred product satisfaction .

In other words, AHP technique and fuzzy theory can be applied to make various choices. In the process, it is possible to deduce product satisfaction through human decision making and provide a system to recommend products. It is necessary to develop a technology capable of calculating the relative importance to the user preference and deducing the product satisfaction that reflects the subjective inclination of the consumer by performing the fuzzy integration of the evaluation value of the product information.

Published Japanese Patent Application No. 10-2002-0038446 (May 23, 2002)

Non-Patent Document 1: Ji Soo Park, Seok Jae Jung, Young Hoon Lee, Kyung Sup Kim. Implementation of rule based insurance product recommendation and design system through fuzzy inference. The Korean Journal of Electronic Commerce Vol. 12, No. 1, 2007. 2: Non-Patent Document 2: A Study on the Selection of Indicators for the Evaluation of New and Renewable Energy Business. POSCO Management Institute, 2010.06.:116-139

In order to solve the above problems, a problem to be solved by the present invention is to perform a pair comparison of a user's preference according to collected contexts, analyze the calculated pair comparison values by AHP, And to provide a fuzzy integral based product satisfaction inference system that can infer product satisfaction by applying a fuzzy integral to the fuzzy measure and product information on the calculated importance, taking into account the subjective tendency.

It is another object of the present invention to provide a fuzzy integral-based product satisfaction inference system that can recommend a product to a user according to an inferred product satisfaction, considering a subjective inclination.

In order to solve the above problems, a fuzzy integral based product satisfaction reasoning system considering a subjective tendency according to the present invention includes a context collecting unit (11) for collecting a context of a user; An AHP analyzer 12 for performing a pair comparison of a user's preference according to the collected context, analyzing the performed pair comparison value by an Analytic Hierarchy Process (AHP) and calculating importance; A fuzzy measure calculating unit 13 for calculating a fuzzy measure according to the calculated importance; And a fuzzy integration unit 15 for calculating the integrated value by applying the calculated fuzzy measure and the product information to the fuzzy integral to thereby infer the product satisfaction with the user's preference.

The fuzzy integral based product satisfaction reasoning system considering the subjective tendency according to the present invention quantitatively analyzes the obtainable situation information with the preference of the user and by using it to deduce the product satisfaction that reflects the subjective inclination of the user, It is possible to minimize the ambiguity occurring in the user's decision making and to have a remarkable effect of recommending the product reflecting the user's subjective opinion.

FIG. 1 is a diagram illustrating an AHP hierarchy for deriving relative importance in a product satisfaction evaluation standard of a fuzzy integral-based product satisfaction inference system that takes into consideration a subjective tendency according to an embodiment of the present invention.
FIG. 2 is a diagram for explaining the Choke integration for the set X. FIG.
FIG. 3 is a schematic view showing a main configuration of a fuzzy integral-based product satisfaction inference system considering a subjective tendency according to an embodiment of the present invention.
FIG. 4 is a flowchart illustrating a method for inferring product satisfaction in a fuzzy integral-based product satisfaction inference system in which a subjective tendency is considered according to an embodiment of the present invention.
FIG. 5 is a graph showing product-specific fuzzy integral values calculated according to product information in a fuzzy integral-based product satisfaction reasoning system in consideration of a subjective tendency according to an embodiment of the present invention.

The terms and words used in the present specification and claims should not be construed as limited to ordinary or dictionary terms and the inventor may properly define the concept of the term in order to best describe its invention It should be construed as meaning and concept consistent with the technical idea of the present invention.

Therefore, the embodiments described in the present specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and are not intended to represent all of the technical ideas of the present invention. Therefore, various equivalents And variations are possible.

Before describing the present invention with reference to the accompanying drawings, it should be noted that the present invention is not described or specifically described with respect to a known configuration that can be easily added by a person skilled in the art, Let the sound be revealed.

The present invention relates to a fuzzy integral-based product satisfaction inference system that takes into consideration the user's subjective tendency, and in order to infer product satisfaction considering user preference, the relative importance of the user's preference is determined by using an Analytic Hierarchy Process And the evaluation value of the degree of satisfaction with the product is calculated by applying the fuzzy integral so that it is possible to deduce the satisfaction degree of the product reflecting the subjective inclination of the user and further the product reflecting the subjective inclination of the user according to the inferred product satisfaction To a system capable of recommending < RTI ID = 0.0 >

Before describing the contents of the present invention in detail, the technology used in the present invention will be described first, and then the constitution of the present invention using this technology will be described.

AHP will be described.

The analytic hierarchy process (AHP) is a multi-criteria decision making model developed by Saaty, which is a systematic analysis of the decision-making process. This is a model for deriving a rational result by performing pairwise comparison and performing pairwise comparisons on the overall evaluation items and calculating relative importance to the evaluation items.

In particular, the AHP supports quantitative analysis of qualitative problems, supporting decision-making in a systematic manner, organizing complex and unclear problems into multiple tiers, and partial relationships by means of pairwise comparisons By evaluating the significance of the value of the product on a quantified scale, allowing decision makers to make more accurate decisions.

Referring to FIG. 1 of the accompanying drawings, the AHP hierarchy required in the process of applying the AHP to the present invention and deriving the relative importance can be referred to.

FIG. 1 is a diagram illustrating an AHP hierarchy for deriving relative importance in a product satisfaction evaluation standard of a fuzzy integral-based product satisfaction inference system that takes into consideration a subjective tendency according to an embodiment of the present invention.

In addition, it can be classified into 5-point scale and 9-point scale according to the numerical scale. In the case of 9-point scale, it can be defined as shown in Table 1 below.

importance Justice One Similar 3 Slightly important 5 Important 7 very important 9 Extremely important 2, 4, 6, 8 Median of defined values

At this time, when the pair comparison matrix is A and the element (evaluation item) of A is a _ij , a _ij can be estimated using the following equation (1).

Where n is the number of elements in the hierarchy to be compared and w _i is the relative importance of the n elements.

Therefore, matrix A with an element a _ij can be expressed as:

Here, A represents a pair of comparison matrices, and a _ij represents an element of the pair comparison matrix A.

In addition, the weight vector w = [w ₁ , w ₂ , w ₃ , ... , w _n ] can be expressed as A · W = n · W.

Where n is the eigenvalue of the matrix A and W is the eigenvector of A.

On the other hand, if the respondent does not consistently respond to the relative importance of each item in the pairwise comparison matrix, the accuracy of the pairwise comparison matrix becomes low. Therefore, the consistency index (CI) and the consistency ratio (CR) should be used to verify whether the importance of pairwise comparison is consistent.

The consistency index (CI) and the consistency ratio (CR) can be obtained by the following Equations (2) and (3).

Here, CI is the coherence index, [lambda] _max is the maximum eigenvalue of the pair comparison matrix, and n is the number of elements to be compared in one layer.

Here, CR is the coherence rate, CI is the coherence index, and RI is the random index (RI).

At this time, the coherence index (CI) has a smaller value as the response of the respondent becomes coherent. If the ratio is less than 0.1, the coherence index is determined to be consistent.

That is, in the present invention, the contexts that can be acquired from the mobile device are collected, and the importance of each context is analyzed using the AHP, so that the relative importance of the context can be calculated.

Fuzzy theory, on the other hand, is a mathematical approach to the process of decision making by the brain in various problems in ambiguous and unclear situations.

It is based on the idea of fuzzy sets introduced by professor LA Zadeh of the University of California at Berkeley in 1965.

The concept of a fuzzy set can be mathematically expressed from binary logic that each object belongs to or does not belong to a certain group by representing the degree to which each object belongs to the group as a membership function.

The fuzzy measure is a mathematical representation of the relationship between a and A, by indicating the ambiguity of the fact that the ambiguous element a in the general set A belongs to the subset P of A. Briefly summarized, it can be said that the number of cases is taken into account and the movement is designated accordingly.

In the present invention, the ambiguity of the language expression possessed by the user's subjective judgment is quantitatively determined using the interaction coefficient fuzzy measure (fuzzy measure) and the fuzzy integral (fuzzy integral) in order to analyze and classify the relationship between the user preference and the product information Quot; is < / RTI >

The fuzzy integral is explained in more detail.

In order to apply the fuzzy integral, a fuzzy measure must be calculated. In the present invention, an interaction coefficient fuzzy measure (? Fuzzy Measure) using an interaction coefficient can be applied.

The interaction coefficient fuzzy measure is widely applied as an evaluation model of the subjective measure by the fuzzy measure proposed by SUGENO, and is generally defined by the following equation (4).

The interaction coefficient fuzzy measure has the same characteristics as the following equation (5) according to the value of [lambda] (interaction coefficient), and is applied as a model of the subjective measure.

The fuzzy integral is an integral application method for the fuzzy measure, and the fuzzy integral applied in the present invention is configured to quantitatively handle the ambiguity of the subjective judgment of the expression language and the evaluator by using the choquet integral, which is one of the fuzzy integrations .

The Choquet Integral is a Functional defined by the French mathematical Choquet on the Capacity and is defined as in Equation (6) below.

Where f is a real function on X and g is a fuzzy measure on X.

Hereinafter, the Choquet Integral will be described in detail.

If a simple function f on X is given by the following Equation 7 with respect to the set X = {x ₁ , x ₂ , x ₃ , x ₄ }

로 나타낸다. i 및 j는 정수이다.
Here, 0 < r ₁ < r ₂ < r ₃ ... <r _n , D _i ∩ D _j = ø (i ≠ j)

Respectively. i and j are integers.

This is shown in FIG.

FIG. 2 is a diagram for explaining the Choke integration for the set X, and the Choquet Integral for the fuzzy measure g of the single function f can be expressed by the following equation (8).

로 표현된다.but,

Lt; / RTI &gt;

Where A is a set, i is an integer, and g (A) is a fuzzy measure for set A.

That is, as shown in the figure, the Choke integral means integrating along the vertical axis representing the real value f that satisfies the additive.

The fuzzy integral based product satisfaction reasoning system according to the present invention collects contexts, compares the collected contexts with each other, compares the performed comparisons with AHP, calculates importance, The fuzzy measure is then calculated, and the calculated product fuzzy measure and product information can be applied to the fuzzy integral to deduce the product satisfaction with the user's preference.

FIG. 3 schematically shows a main configuration of a fuzzy integral-based product satisfaction inference system considering a subjective tendency according to an embodiment of the present invention. The product satisfaction inference system 10 includes a context collector 11, an AHP analysis A fuzzy integral calculation unit 13, a product information storage unit 14, a fuzzy integration unit 15, and a product satisfaction estimating unit 16, as shown in FIG.

FIG. 4 is a flowchart illustrating a method for inferring product satisfaction in a fuzzy integral-based product satisfaction inference system in which a subjective tendency is considered according to an embodiment of the present invention.

Hereinafter, the present invention will be described with reference to the configurations shown in FIG. 3 and FIG.

The fuzzy integral based product satisfaction reasoning system considering the subjective tendency according to the present invention may be constituted by an application installed in a mobile (not shown in the drawing) or a server connected to a mobile (not shown in the drawing).

The context collecting unit 11 collects the obtainable user's preference information context.

Here, the context can be obtained from the mobile. Mobile refers to mobile devices such as smart phones and tablet PCs that can be moved with the user by the user.

The context obtainable from such a mobile may be ① mobile, a context collected from a web site, or ② a context collected from user's input information.

For example, the context collected from mobile and web sites is empirically recognized in a specific situation. When a user uses social commerce through mobile, he or she empirically recognizes events occurring in the mobile and collects them in a linguistic context .

According to the design conditions, it is possible to configure the product to be recommended by predicting the behavior of the user based on the position of the user through the configuration of the GPS provided in the mobile.

For example, the position of the user is grasped through the GPS configuration. At this time, when the position of the user is rapidly changed but the speed and the cycle are different from the movement of the vehicle, it can be confirmed that the user is running.

In addition, in order to empirically recognize this, it is possible to continuously observe information about the positional change of the user, and to arrange for the user to recommend items (running shoes, running shoes, knee guard, running suit, etc.) necessary for running .

As another example, when collected from the user's input information, the context can be collected by receiving input of the price, the discount rate, the number of purchasers, and the customer satisfaction of the user's preferred goods.

Here, the price may be the discounted amount of the commodity (cost-discount price, for example, 60% discount, the cost is 10,000, the discount price is 4,000 won, and the discounted amount is 6,000 won).

This is because the discount rate, the number of buyers, and the customer satisfaction, excluding the price, tend to increase the preference as the value increases and the price tends to increase as the price decreases.

Also, the customer satisfaction may be the product satisfaction that was previously deduced by the relative importance according to the preference of the other user for the product.

The AHP analyzer 12 calculates a weight for the importance by comparing the preferences of the user based on the collected contexts for each context in pairs. [Table 2] to [Table 5] The result of the experiment that calculates the weight by comparing the preference information with each other.

At this time, the user's preference in [Table 2] to [Table 5] was used as the expression language defined as "price", "discount rate", "number of buyers" and "customer satisfaction".

[Table 2] through [Table 5] are four cases (cases) in which discount rate, price, product satisfaction, and number of buyers are evaluated to be highly evaluated as relative importance, respectively.

In other words, [Table 2] to [Table 5] are derived from the relative importance among the evaluation criteria using the AHP technique and are evaluated according to the subjective decision, so that the individual tendency is directly reflected.

(Case 1) price discount rate Buyers Customer Satisfaction weight price 3.0 4.0 3.0 0.263 discount rate 4.0 2.0 0.511 Buyers 5.0 0.066 Customer Satisfaction 0.160

In Table 2, the relative importance of price and number of buyers is 4.0 (4 out of 9 points of Professor Sati's scale). This means that the price is 4 more important than the number of buyers.

Conversely, the relative importance between price and discount rate can be seen as 3.0 using italics, which means that the price is 1/3 of the discount rate.

This interpretation is also the same in the following [Table 3] to [Table 5], and the relative importance of each case is expressed as a weight, and it can be considered more importantly in evaluating the satisfaction of the commodity by the weight.

(Case 2) price discount rate Buyers Customer Satisfaction weight price 3.0 6.0 3.0 0.510 discount rate 5.0 3.0 0.282 Buyers 4.0 0.056 Customer Satisfaction 0.152

(Case 3) price discount rate Buyers Customer Satisfaction weight price 3.0 4.0 3.0 0.165 discount rate 4.0 2.0 0.312 Buyers 5.0 0.066 Customer Satisfaction 0.457

(Case 4) price discount rate Buyers Customer Satisfaction weight price 3.0 5.0 3.0 0.077 discount rate 4.0 3.0 0.144 Buyers 2.0 0.490 Customer Satisfaction 0.289

[Table 5] shows that the number of buyers is considered as an important factor for evaluating the satisfaction of the commodity. In addition, the customer satisfaction is also considered to be relatively more important than the price and discount rate .

The fuzzy measure calculator 13 is for calculating the fuzzy measure and is calculated using the importance (AHP weight) calculated by the AHP analyzer 12 and the interaction coefficient (?).

Here, the algorithm for calculating the fuzzy measure using the calculated importance and the interaction coefficient can be calculated using Singleton Fuzzy Measure Ratio Standard.

The interaction coefficient (?) Means a correlation between contexts. In the present invention, a value of 3.0001 can be used.

In order to calculate the fuzzy measure using the calculated importance and the interaction coefficient, a fuzzy integral is applied based on information such as a price (preferably a discounted amount) or a discount rate, which are comparable elements common to a plurality of products At this time, the elements such as the above-mentioned price or discount rate are different from each other in kind and unit of measurement, so normalization is required.

For this normalization, the price can be obtained, for example, by the following equation.

Here, the MIN () and MAX () functions are functions for extracting the minimum value and the maximum value from the entire data series.

The normalized value as described above can be utilized as product information necessary for fuzzy integral application. That is, it is necessary to infer the satisfaction degree of the product reflecting the user's subjective through the fuzzy integral unit 15. The product information to be utilized for this is calculated through the above-mentioned equation (9).

The fuzzy integration unit 15 calculates the integrated value of the product information by applying the calculated fuzzy measure and the fuzzy integral to the product information stored in the product information storage unit 14. [ At this time, the fuzzy integral can be performed using the Choquet Integral.

At this time, the basic information of the goods stored in the product information storage unit 14 may be stored as information such as cost, discount price, discounted amount, discount rate, number of purchasers, and customer satisfaction.

For example, the product information storage unit 14 can store basic information of a product as shown in [Table 6]. At this time, in Table 6, only basic information about 15 kinds of goods is displayed to illustrate an example according to the present invention.

Herein, the product information and the basic information of the product are described in this specification. The basic information of the product may mean the information of the product which is not normalized as shown in the following [Table 6] Can be expressed as the normalized product information through Equation (9).

No cost Discount price Discounted amount discount rate Buyers Product satisfaction One 48,000 34,560 13,400 28 348348 76.0 2 62,000 43,400 18,600 30 792 72.0 3 55,000 41,250 13,750 25 1,203 82.0 4 89,000 71,200 17,800 20 241 92.0 5 110,000 88,000 22,000 20 88 84.0 6 32,000 27,200 4,800 15 397 66.0 7 38,000 28,500 9,500 25 361 88.0 8 75,000 51,000 24,000 32 531 86.0 9 53,000 42,400 10,600 20 43 62.0 10 99,000 74,250 24,750 25 360 68.0 11 183,000 107,970 75,030 41 58 81.0 12 145,000 123,250 21,750 15 20 74.0 13 28,000 23,240 4,760 17 574 86.0 14 99,000 89,100 9,900 10 46 80.0 15 104,000 72,800 31,200 30 77 90.0

If the basic information of the commodity as shown in [Table 6] is normalized by the above-mentioned Equation (9), the normalized value is calculated as indicated in the "Normalized Value (Normalized Value)" can do.

FIG. 5 is a graph showing product-specific fuzzy integral values calculated according to product information in a fuzzy integral-based product satisfaction reasoning system in consideration of a subjective tendency according to an embodiment of the present invention.

In addition, on the right side of FIG. 5 of the accompanying drawings, the respective experimental results for the above [Table 2] to [Table 5] evaluated by AHP with the Choquet Integral are shown as evaluation values (Choquet Integrated Values ).

In Case 1, the weight for the discount rate (L: 0.510) and the discounted amount (L: 0.263) is high and for Case 2, the discounted amount (L: 0.510) The weight is set high.

In Case 3, the weight of the purchase satisfaction (L: 0.457) and the discount rate (L: 0.312) were set high. In Case 4, the number of buyers (L: 0.490) The weight was set high and the experiment was conducted.

As a result, as shown in FIG. 5, in the cases 1, 2 and 3, the satisfaction rate of the products 8, 11, and 15 having a large discount rate and discounted price was high. In case 4, The satisfaction level of the students was high.

The product satisfaction estimating unit 16 deduces the highly evaluated merchandise satisfaction through the fuzzy integration unit 15 as described above, and may be included in the fuzzy integration unit 15 depending on design conditions.

Meanwhile, the fuzzy integral based product satisfaction reasoning system considering the subjective tendency according to the present invention may be configured to recommend the product to the user (consumer) according to the product satisfaction deduced by the product satisfaction reasoning unit 16.

This is configured to recommend a product that the user is "most favored " or" relatively most important "according to the inferred commodity satisfaction, thereby detecting the commodity having the highest evaluated value of the commodity satisfaction .

This can be done through the product satisfaction reasoning unit 16 or between the product satisfaction reasoning unit 16 and the recommended product display unit 20 in Fig. 2 by further configuring a recommended product detecting unit (not shown in the figure) You may.

At this time, the number of goods to be detected may be a single product having the highest evaluation value, and the top two to five products may be recommended.

However, the present invention is not limited to the above two to five products. It is needless to say that, according to the design conditions, it is possible to constitute a recommendation of five or more higher-ranked products in the order of higher evaluation value for the satisfaction of the product.

The recommendation merchandise display unit 20 displays a recommended merchandise according to the estimated satisfaction of the merchandise. When the system is applied to the system mobile according to the present invention, the recommended merchandise can be displayed through the display of the mobile.

In addition, although not directly described in the present specification, inferred product satisfaction can be configured to be displayed on an applicable device (mobile, etc.) such as a recommended product, based on the purpose, structure, and effects of the present invention Of course, can be configured to be stored in a storage means such as a server.

According to other design conditions, if a list of products excluded from the browse list exists while the user is using a specific social commerce, the recommendation product display unit 20 may display the product recommendation target Can be configured to be excluded from.

For example, suppose that a user has set a specific product to be excluded from browsing through its own function of social commerce due to other circumstances regardless of "price", "discount rate", "number of buyers" and "customer satisfaction" .

For example, according to the above factors, a weight for relative importance may be calculated and offered as a commodity to be recommended, but if the user refuses to recommend it again because the user has not tried it, You may refuse to view the goods.

In this case, according to the present invention, the information of the user of the social commerce is collected through the context collecting unit 11 and is interlocked with the social commerce, so that the user can know the product refused to receive the recommendation. It is determined that the product is not recommended.

As another example, if the user refuses to receive a recommendation, the product may be designed so that the product is not included in the basic information of the product for performing the above-described process.

The fuzzy integral-based product satisfaction reasoning system considering the subjective tendency according to the present invention as described above quantitatively analyzes the obtainable situation information with the user's preference and uses it to calculate the product satisfaction , It is possible to minimize the ambiguity occurring in the user's decision making and to recommend the product reflecting the user's subjective opinion.

1 to 5 are merely illustrative of the essential features of the present invention. As various designs can be made within the technical scope thereof, the present invention can be applied to the configurations and functions of Figs. 1 to 5 It is self-evident that it is not limited.

10: Product Satisfaction Reasoning System
11: Context collecting unit
12: AHP analysis section
13: Fuzzy measure calculator
14: Item information storage unit
15: Fuzzy integral part
16: Product Satisfaction Reasoning Department
20: Recommended product indicator

Claims (5)

A context collecting unit 11 for collecting a user's context;
An AHP analyzer 12 for performing a pair comparison of a user's preference according to the collected context, analyzing the performed pair comparison value using an Analytic Hierarchy Process (AHP), and calculating importance;
A fuzzy measure calculating unit 13 for calculating a fuzzy measure according to the calculated importance; And
A fuzzy integration unit 15 for applying the calculated fuzzy measure and product information to a fuzzy integral to calculate an integrated value; And estimating a product satisfaction with the user's preference,
The context comprises:
It is basic information of a product composed of information collected when a user purchases a product on the Internet and information directly input by the user,
The product information includes:
Wherein the information is normalized using basic information of a product including a cost of the product, a discount price, a discounted price, a discount rate, a number of buyers,
A Fuzzy Integral - Based Product Satisfaction Reasoning System Considering Subjective Disposition.
The method according to claim 1,
The fuzzy measure is a measure of
Wherein the product is calculated by using an interaction coefficient between the collected contexts and the importance calculated by analyzing with an AHP (Analytic Hierarchy Process).
The method according to claim 1,
The user preference,
Price, discount rate, number of purchasers, and customer satisfaction, based on fuzzy integral.
The method of claim 3,
The above-
Wherein the discounted amount is a discounted amount obtained by subtracting the discounted price from the cost of the product. The fuzzy integral based product satisfaction inference system considers the subjective tendency.
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