KR20170024257A - Method and apparatus for recommending personalized subject - Google Patents

Method and apparatus for recommending personalized subject Download PDF

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KR20170024257A
KR20170024257A KR1020150119285A KR20150119285A KR20170024257A KR 20170024257 A KR20170024257 A KR 20170024257A KR 1020150119285 A KR1020150119285 A KR 1020150119285A KR 20150119285 A KR20150119285 A KR 20150119285A KR 20170024257 A KR20170024257 A KR 20170024257A
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윤장혁
김충일
신지훈
허유진
최남규
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건국대학교 산학협력단
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Abstract

The method of recommending a personalized course recommends a method of calculating a degree of similarity between the recommendation target person and the plurality of information providers by calculating at least one of a jacquard coefficient and a cosine coefficient for a user group including a recommendation target person and a plurality of information providers Calculating a predicted preference of the recommending target person for the at least one subject based on the calculated degree of similarity and the evaluation score of the plurality of information providers for at least one subject, Determining the top N subjects as a recommended subject, and providing the recommended subject to the recommended subject.

Description

 [0001] METHOD AND APPARATUS FOR RECOMMENDING PERSONALIZED SUBJECT [0002]

The present invention relates to a method and apparatus for recommending a personalized course.

College liberal arts courses cover humanities, sociology, natural sciences, etc. By taking courses in various fields, students can acquire basic knowledge and personality no matter which major they choose. For students, the choice of liberal arts courses is not only a first step towards becoming a convergent talent through the acquisition of interdisciplinary knowledge, but also influences the overall course achievement and university life satisfaction by influencing other courses taken during the semester Element.

According to the 2012 survey of the Institute of Education, 2.1 million students are currently enrolled at 201 four-year universities nationwide. However, students have difficulty in applying for enrollment because they do not know whether the courses are suitable for their own preference, even if they have the opportunity to enroll in various courses.

In addition, there is not much research on the recommendation system that will help college students to take liberal arts courses. In the case of research on recommendation of university liberal arts subject to the existing collaborative filtering, it is vulnerable to the cold start problem which can not be effectively recommended to the students who do not have the history of the current course due to the application of the collaborative filtering through the simple Pearson correlation coefficient. Cold Start Problem means that the recommendation system does not provide accurate recommendation due to insufficient information of newly appeared users.

Therefore, according to the recommendation method based on the collaborative filtering, it is difficult to apply to the students who have relatively few courses in the liberal arts, And the preference and tendency of the curriculum rather than the preference of the course will be subject to limitations.

The background technology of the present application is disclosed in Korean Patent Publication No. 2007-0091843.

It is an object of the present invention to provide a method and a device for recommending a personalized course to recommend a more suitable course to a student who has a short history while maximizing the preference and tendency of an individual.

It is to be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.

As a technical means for achieving the above technical object, a method of recommending a personalized class subject according to an embodiment of the present invention is a method of recommending a class of a user including a recommendation target person and a plurality of information providers by using a Jaccard coefficient and a cosine coefficient Calculating a degree of similarity between the recommended person and the plurality of information providers by calculating at least one of the plurality of information providers based on the degree of similarity calculated and the evaluation scores of the plurality of information providers with respect to the at least one subject, Calculating predicted preferences of the recommendable subject for the subject, determining the top N subjects with the highest predicted preference as a recommended subject, and providing the recommended subject to the recommended target person.

According to an example of this embodiment, before calculating the degree of similarity, the user group is divided into a plurality of groups based on at least any one of the number of courses taken by the plurality of information providers or the grade of the plurality of information providers And a step of sorting.

According to an example of this embodiment, the method may further include determining whether the recommended target person belongs to any one of the plurality of groups based on at least any one of the number of the classes or the grade of the recommended target person .

According to an example of this embodiment, the step of calculating the degree of similarity may include calculating at least one of the jacquard coefficient and the cosine coefficient according to which group of the plurality of groups the recommended target person belongs to and calculating the degree of similarity . ≪ / RTI >

According to an embodiment of the present invention, the step of calculating the degree of similarity may be performed by determining weights of the Jacquard coefficients and the cosine coefficients according to the number of the plurality of groups.

According to an embodiment of the present invention, the grouping of the user groups may include classifying the information providers whose number of courses taken less than a predetermined first threshold value among the plurality of information providers into a first group, Classifying the information providers that exceed the first threshold value and are equal to or less than the preset second threshold value into the second group and classify the information providers whose number of the taken classes exceeds the second threshold value into the third group .

According to an example of this embodiment, the step of determining whether the recommended target person belongs to any one of the first group to the third group based on at least any one of the number of classes or the grade of the recommended target person .

According to one example of this embodiment, the step of calculating the degree of similarity may include calculating the similarity between the recommended person and the plurality of information providers by calculating the Jacquard coefficient when the recommended person belongs to the first group, Calculating a degree of similarity between the recommended person and the plurality of information providers by calculating the Jacquard coefficient and the cosine coefficient when the recommended person belongs to the second group; and when the recommended person belongs to the third group, And calculating the similarity between the recommended person and the plurality of information providers.

According to an embodiment of the present invention, the step of calculating the predicted preference may include calculating an average value of the evaluation scores of the recommendation target person for the subject taken by the recommendation target person and the average value of the plurality of information about the subject taken by the plurality of information providers And the average value of the evaluation scores of the provider.

According to one example of this embodiment, the jacquard coefficient is calculated based on the attribution information of the recommendation target person and the plurality of information providers, and the attribution information is acquired when the recommendation target person and the plurality of information providers are preferable A subject, an area of interest, a field of hobby, and a major.

According to an embodiment of the present invention, the method further includes a step of determining a top K name having a high degree of similarity with the recommending target person among the plurality of information providers, wherein the step of calculating the predicted preference includes: The degree of similarity, and the number of evaluation scores of the K users.

As a technical means for achieving the above technical object, a personalized course recommendation apparatus according to an embodiment of the present invention calculates at least one of a jacquard coefficient and a cosine coefficient for a user group including a recommended person and a plurality of information providers A similarity degree calculating unit for calculating a degree of similarity between the recommendation target person and the plurality of information providers based on the calculated similarity and the evaluation score of the plurality of information providers with respect to at least one subject, A prediction preference calculating unit for calculating a prediction preference of the recommendation target person, a decision unit for deciding the recommendation subject matter as the recommendation subject matter, and a providing unit for providing the recommended subject matter to the recommendation subject person.

According to an embodiment of the present invention, the information processing apparatus may further include a classifying unit for classifying the user groups into a plurality of groups based on at least one of the number of courses taken by the plurality of information providers or the grade of the plurality of information providers .

According to one example of this embodiment, the classifying section determines whether the recommending target person belongs to any one of the plurality of groups based on at least any one of the number of classes or the grade attended by the recommending target person, Wherein the degree of similarity is calculated by selecting at least one of the jacquard coefficient and the cosine coefficient according to which group of the plurality of groups the recommended target person belongs to.

According to an example of this embodiment, the classifying unit classifies the information providers whose number of courses taken less than the first threshold value is equal to or less than a predetermined threshold, among the plurality of information providers, and classifies the information provider into the first group, And the information provider having a preset second threshold value or less is classified into the second group and the information providers whose number of the taken courses exceeds the second threshold value are classified into the third group.

According to an example of this embodiment, the classifying unit determines whether the recommended target person belongs to any one of the first to third groups based on at least any one of the number of classes or the grade of the recommended subjects , The similarity calculating unit calculates the similarity between the recommendation target person and the plurality of information providers by calculating the Jacquard coefficient when the recommendation target belongs to the first group, and when the recommendation target belongs to the second group , Calculating the Jacquard coefficient and the cosine coefficient to calculate a degree of similarity between the recommended person and the plurality of information providers, and, when the recommended person belongs to the third group, calculating the cosine coefficient, The degree of similarity between the information providers of the present invention can be calculated.

According to an example of this embodiment, the similarity calculating unit determines a top K name having a high degree of similarity with the recommended target person among the plurality of information providers, and the prediction preference calculating unit calculates the similarity between the recommended target person and the K name and the K And calculating the predicted preference of the recommendation target person for the at least one subject based on the evaluation score of the recommendation target person.

According to one embodiment of the present invention, at least one of a jacquard coefficient and a cosine coefficient is set for a user group including a recommended person and a plurality of information providers, Calculating a degree of similarity between the recommendation target person and the plurality of information providers based on the degree of similarity and the evaluation score of the plurality of information providers with respect to the calculated degree of similarity and at least one subject, A step of calculating a predicted preference of the subject, a step of determining the top N subjects with the highest prediction preference as a recommended subject, and a step of providing the recommended subject to the recommended person Computer-readable May include a recording medium.

The above-described task solution is merely exemplary and should not be construed as limiting the present disclosure. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and the detailed description of the invention.

According to the task solution of the present invention described above, it is possible to recommend a more suitable course even to a student who lacks the history of the course while reflecting the preference and propensity of the individual as much as possible according to the grade of the recommended person and the history of the course.

The effects obtainable herein are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description .

FIG. 1 is a block diagram schematically illustrating the configuration of a personalized course recommendation system according to an embodiment of the present invention.
2 is a block diagram of a personalized course recommendation apparatus according to an embodiment of the present invention.
3 is a flowchart illustrating a method of recommending a personalized course according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. It should be understood, however, that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In the drawings, the same reference numbers are used throughout the specification to refer to the same or like parts.

Throughout this specification, when a part is referred to as being "connected" to another part, it is not limited to a case where it is "directly connected" but also includes the case where it is "electrically connected" do.

Throughout this specification, when a member is " on " another member, it includes not only when the member is in contact with the other member, but also when there is another member between the two members.

Throughout this specification, when an element is referred to as "including " an element, it is understood that the element may include other elements as well, without departing from the other elements unless specifically stated otherwise.

The terms "about "," substantially ", etc. used to the extent that they are used throughout the specification are intended to be taken to mean the approximation of the manufacturing and material tolerances inherent in the stated sense, Accurate or absolute numbers are used to help prevent unauthorized exploitation by unauthorized intruders of the referenced disclosure. The word " step (or step) "or" step "used to the extent that it is used throughout the specification does not mean" step for.

In this specification, the term " part " includes a unit realized by hardware, a unit realized by software, and a unit realized by using both. Further, one unit may be implemented using two or more hardware, or two or more units may be implemented by one hardware.

In the present description, some of the operations or functions described as being performed by a terminal, a device, or a device may be performed instead in a server connected to the terminal, device, or device. Likewise, some of the operations or functions described as being performed by the server may also be performed in a terminal, device or device connected to the server. Hereinafter, one embodiment of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a block diagram schematically illustrating the configuration of a personalized course recommendation system according to an embodiment of the present invention.

1, the personalized course recommendation system 100 according to an exemplary embodiment of the present invention includes a recommended person 110, a user group including a plurality of information providers 120, and a personalized course recommender 130 ). The recommendation target 110 and the plurality of information providers 120 may each have a terminal, and each terminal and the personalized course recommender 130 may be interconnected through a network.

For example, the terminal may be a wireless communication device that is guaranteed to be portable and mobility, such as a Personal Communication System (PCS), a Global System for Mobile communications (GSM), a Personal Digital Cellular (PDC), a Personal Handyphone System (Personal Digital Assistant), IMT (International Mobile Telecommunication) -2000, CDMA (Code Division Multiple Access) -2000, W-CDMA (W-CDMA), Wibro (Wireless Broadband Internet) ), And the like, for example. In addition, the terminal may include a computer connected to a remote server through a network or connectable to other terminals and a server. The computer may include, for example, a notebook computer, a desktop computer, a laptop computer, a tablet computer, and the like, each of which is equipped with a Web browser.

In addition, the network means a connection structure in which information can be exchanged between each node such as a plurality of terminals and servers. One example of such a network is a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) Network, a World Interoperability for Microwave Access (WIMAX) network, the Internet, a LAN, a Wireless Local Area Network (WAN), a Wide Area Network (WAN), a Personal Area Network (PAN), a Bluetooth ) Network, a satellite broadcast network, an analog broadcast network, a DMB (Digital Multimedia Broadcasting) network, and the like.

In this specification, the recommended person 110 refers to a user who is provided with a recommended subject by the personalized subject recommendation device 130, and the plurality of information providers 120 include a personalized subject recommendation device 130, (Information) necessary for recommending the user.

The personalized course recommender 130 can collect attribute information such as a field of interest of a plurality of the information providers 120 through a questionnaire and scores of preference (evaluation) for the courses so far. In addition, the personalized course recommender 130 recommends a recommended subject 110 to the recommended subject 110 by using the collected information, but differs depending on the characteristics, grades, and history of the recommended subject 110, Can be recommended. Hereinafter, a process of recommending a recommendation subject 110 to a recommended subject 110 will be described in detail with reference to the personalized subject recommendation apparatus 130 according to an embodiment of the present invention.

The personalized course recommender 130 may classify a plurality of user groups including the information provider 120 based on at least any one of the number of courses attended by a plurality of information providers 120 or the grade of a plurality of information providers 120 And the like. According to one embodiment of the present invention, the personalized course recommender 130 classifies information providers whose number of taken courses is equal to or less than a first threshold value set in advance among the plurality of information providers 120 into the first group, The information providers whose number of subject lines exceeds the first threshold value and are equal to or less than the second threshold value are classified into the second group and the information providers whose number of the taken courses exceeds the second threshold value may be classified into the third group .

For example, the personalized course recommender 130 classifies the information providers having three or less courses in the plurality of information providers 120 into the first group, and classifies the information providers having 4-6 courses 2 groups, and the information providers having more than 7 courses can be classified into the third group. In the above example, the number of groups is three, but the number of groups in which the user group is classified may be variously determined according to the grade of the information providers 120, Also, the number of subjects (the first threshold value, the second threshold value, and the third threshold value) used in the determination of the group can be variously determined according to the grade of the information providers 120,

In addition, the individual-customized course recommendation apparatus 130 determines whether the recommendation target person 110 belongs to any one of the above-mentioned plural groups based on at least one of the number of classes or the grade of the recommended subject 110 . For example, if the recommended subject 110 has five courses, the recommendation apparatus 130 can determine that the recommended subject 110 corresponds to the second group.

The personalized course recommender 130 calculates at least one of a jacquard coefficient and a cosine coefficient for a user group including the recommended person 110 and the plurality of information providers 120 to obtain a recommended person 110 and the plurality of information providers 120 can be calculated.

The Jacquard coefficient is used in a content-based filtering technique. The individual-customized course recommendation device 130 performs recommendation based on recommendation object 110 and a plurality of information providers 120, It is possible to calculate the similarity between each of the recommendation target 110 and the information provider 120 by calculating the jacquard coefficient between the target person 110 and the information provider 120. [ For example, the attribute information may include at least one of a subject, an area of interest, a field of hobby, and a major that the recommended subject 110 and a plurality of the information providers 120 are preferred or have excellent grades. , The jacquard coefficient is calculated by the following equation (1). If the characteristics are consistent between the two users, the jacquard coefficient is 1; For example, if user u1 checks Korean, English, and society as preferred courses among 4 courses (Korean, English, Society, and Mathematics) and user u2 selects English, If mathematics is checked, the similarity between the user u1 and the user u2 can be calculated to be 0.25.

[Equation 1]

Figure pat00001

The cosine coefficient is used in a collaborative filtering technique. The cooperative filtering technique uses the evaluation value of the recommendation target 110 for the subject and the evaluation value of another user (a plurality of information providers 120) 110) recommends a preferred course.

For example, as shown in [Figure 1] below, the collaborative filtering technique can recommend a recommendation target or a plurality of information providers (u) using scores evaluated for a plurality of subjects (i) . The personalized course recommender 130 calculates a cosine coefficient between the recommended person 110 and the information provider 120 based on the evaluation score for the recommended subjects 110 and the subjects given by the plurality of information providers 120 The degree of similarity between the recommendation target person 110 and the information provider 120 can be calculated.

[Figure 1]

Figure pat00002

The cosine coefficient can be calculated by internally interpolating cosine of two vectors as shown in Equation (2) by treating the recommended object A and the user V (information provider) as vectors.

&Quot; (2) "

Figure pat00003

If the two vectors are similar, that is, if the preference of the user A is similar to the preference of the user V, the value of the cosine similarity value is close to 1, otherwise the value is close to zero.

For example, if a liberal arts subject to the recommendation subject (A) positively evaluates is law and society, the technique of conversation, modern man's diet, and the user (V) is positive in law and society, In the case of evaluation, the cosine coefficient between the recommendation target person A and the user V can be calculated by the following equation (3).

&Quot; (3) "

Figure pat00004

Hereinafter, a method of calculating the degree of similarity between the recommendation object 110 and the plurality of information providers 120 will be described in detail.

The personalized course recommender 130 selects at least one of the Jacquard coefficient and the cosine coefficient according to which group of the plurality of groups the recommendation target 110 belongs to, and transmits the recommendation target 110 and the plurality of information providers 120 can be calculated.

According to an embodiment of the present invention, when the recommendation subject 110 belongs to the first group, the personalized course recommender 130 calculates the Jacquard coefficient and transmits the recommendation object 110 and the plurality of information providers 120, Can be calculated. The reason that the recommendation target person 110 belongs to the first group is because the recommended target person 110 has a small number of subjects that are lower grade (for example, first or second grade university) or relatively lectured and data used for the collaboration filtering technique It can mean that it is relatively small. In other words, when the recommended target person 110 belongs to the first group, the calculation of the cosine coefficient due to the Cold Start Problem is impossible or meaningless. Therefore, the personalized course recommender 130 can perform the content based filtering using the Jacquard coefficients You can recommend the course through.

If the recommendation subject 110 belongs to the second group, the personalized course recommender 130 may calculate the jacquard coefficient and the cosine coefficient to calculate the degree of similarity between the recommendation target 110 and the plurality of information providers 120 Can be calculated. The fact that the recommendation target person 110 belongs to the second group means that the accuracy of the collaboration filtering technique is relatively low because the cosine similarity is significant but the data is insufficient. The personalized course recommender 130 can improve the recommendation accuracy by performing content filtering based on Jacquard coefficients in order to improve the accuracy of the recommendation of the course even if the cooperative filtering using the cosine coefficient can be applied.

The personalized course recommender 130 may determine the weight of the Jacquard coefficient and the cosine coefficient according to the number of the plurality of groups. That is, when calculating the degree of similarity between the recommendation target 110 and the plurality of information providers 120 by using both Jacquard coefficients and cosine coefficients, the personalized course recommender 130 reflects the weight of the Jacquard coefficient and the cosine coefficient , The weight may be determined according to the number of groups in which the user group is classified.

For example, when the user group is classified into three groups, the personalized course recommender 130 may weight the jacquard coefficient and the cosine coefficient at a weight ratio of 1: 1 and recommend the recommendation object 110 and the plurality of information providers 120, Can be calculated. For example, when the user group is classified into five groups, the personalized course recommender 130 increases the Jacquard coefficient and the cosine coefficient by a weight ratio of 2: 1 for the second group, The similarity between the recommendation target 110 and the plurality of information providers 120 can be calculated by weighting the ratio of 1: 1 by weight, and by weighting by 1: 3 with respect to the fourth group.

The personalized course recommender 130 may calculate the cosine coefficient to calculate the degree of similarity between the recommendation target 110 and the plurality of information providers 120 when the recommendation target 110 belongs to the third group have. The reason that the recommended target person 110 belongs to the third group is because the recommended target person 110 is an older student (for example, a third or fourth grade student) or has a relatively large amount of data, The similarity is significant and it can mean that the recommendation of the good subject can be done by the collaborative filtering technique. If the accuracy of the cooperative filtering is determined to be sufficiently high based on the history of the recommendation target 110 or the like, the personalized course recommender 130 can recommend the course using only cooperative filtering using the cosine coefficient.

Hereinafter, a method for calculating the predicted preference of a recommendable subject for a subject based on the calculated degree of similarity or the like will be described in detail.

The personalized course recommender 130 is a program for recommending a recommendation to the recommendation person 110 based on the degree of similarity between the recommended person 110 and the plurality of information providers 120 and the score of the plurality of information providers 120 with respect to at least one subject, Based on at least any one of an average value of the evaluation scores of the recommended subjects 110 and a mean value of the evaluation scores of the plurality of information providers with respect to the subjects taken by the plurality of the information providers 120, It is possible to calculate the predicted preference of the recommendation target person 110 with respect to the subject of the present invention.

According to one embodiment of the present invention, the personalized course recommender 130 determines a top K name having a high degree of similarity to the recommendation target 110 among the plurality of information providers 120. [ In addition, the personalized course recommender 130 can predict the subject preference of the recommendation target 110 based on the similarity degree between the recommended observer 110 and the K name, and the evaluation score for the K subjects.

The predictive preference of the subject according to an embodiment of the present invention can be calculated through the following equation (4).

&Quot; (4) "

Figure pat00005

Figure pat00006
Is a score that predicts how much the recommendation candidate A will prefer the subject i, and predicts the weighted average of the evaluation value of the KNN (K Nearest Neighbor) of the recommendation subject A and the similarity degree with the recommendation subject A. KNN is K users who have the most similar preference (the user with the highest similarity) and K other users (for example, K information providers with high similarity with the recommendation target A).
Figure pat00007
Is an average value of the available evaluation values of the recommendation target person A, and indicates the tendency of the recommendation target person A. As a result, the expected evaluation score to be provided to the recommendation list can be applied to the individual tendency.
Figure pat00008
Does not affect relative size comparisons, but recommends A
Figure pat00009
, It is possible to provide the recommendation score to the recommendation score reflecting the tendency of the recommendation target person. For example, in the case of the recommended subject u1 in [Figure 1]
Figure pat00010
(1 + 5 + 5) / the total number of evaluation subjects (3) is 3.667. 3.667 indicates that the recommendation target u1 has a tendency to give a high rating to the recommendation target u1 as the average of the recommendation target u1, which can be reflected in the expected rating value shown in the recommendation list. If the history data of the recommendation target person A is not sufficient (that is, when the Jaccard similarity is used)
Figure pat00011
It is possible to calculate and calculate the average value of the available evaluation values of all the users (the plurality of information providers 120) as the representative value.

Figure pat00012
Is a usable rating for the subject i of the user v (e.g., K persons with high similarity among the plurality of information providers 120)
Figure pat00013
May be the average of the ratings for each of the K subjects exhibiting the highest degree of similarity.

w (A, v) is a similarity value between the recommendation target person A and the user v (information provider), jacquard coefficients may be used for content-based filtering, and cosine coefficients may be used for collaborative filtering. According to Equation (4), the preference score for the subject i of the information provider v can be added up by using the similarity degree w (A, v) between the recommended person A and the information provider v as a weight. If the similarity of the recommendation object A and the information provider v is high, the value of w (A, v)

Figure pat00014
Is reflected more, and if the degree of similarity is low, it is reflected less.

As described above, according to an embodiment of the present invention, the degree of similarity between the recommendation target person A and all other information providers is calculated, the K information providers having the highest degree of similarity are selected, and the scores of the K similarity data and the subject are used It is possible to calculate the predicted preference of the recommended subject A's subject. By using only K data with high similarity, it is possible to prevent the accuracy of the recommendation system from decreasing due to the recommendation due to the information of the users having different tendencies from the recommendation target. (Top-K type)

In addition, the individual-customized course recommender 130 may determine the top N courses with high predictive preference as the recommended courses, and provide the determined recommended courses to the recommendation target 110. [ For example, the personalized course recommender 130 can determine N top-level subjects having a high degree of preference for the recommendation candidate A for at least one subject i calculated using Equation (4). (Top-N technique)

In addition, the accuracy of the recommendation of the recommended subject 110 can be changed according to the number K of the information providers and the number N of recommended subjects to be considered in calculating the prediction preference.

2 is a block diagram of a personalized course recommendation apparatus according to an embodiment of the present invention. 2, the personalized course recommender 200 according to an exemplary embodiment of the present invention includes a classifier 210, a similarity calculator 220, a prediction preference calculator 230, a determiner 240 ) And a feeder 250. [ 2 can be modified into various forms based on the components shown in FIG. 2. It is to be understood that the present invention is not limited to the above-described embodiments, Those skilled in the art will understand the present invention. For example, the components and functions provided within the components may be combined into a smaller number of components or further separated into additional components.

The classifying unit 210 classifies a user group including a plurality of information providers 120 into a plurality of information providers 120 based on at least any one of the number of courses attended by a plurality of information providers 120 or the grades of a plurality of information providers 120 Group. According to an embodiment of the present invention, the classifying unit 210 classifies the information providers having the number of courses taken less than a predetermined first threshold value among the plurality of information providers 120 into a first group, The information providers that exceed the first threshold value and are equal to or less than the predetermined second threshold value may be classified into the second group and the information providers whose number of the taken courses exceeds the second threshold value may be classified into the third group

In addition, the classifying unit 210 can determine whether the recommending target person 110 belongs to any one of the plurality of groups based on at least one of the number of classes or the grade of the recommended target person 110. According to an embodiment of the present invention, the classifying unit 210 classifies the recommending target 110 as one of the first group to the third group based on at least one of the number of classes or the grade of the recommended subjects 110 It can be decided whether or not it belongs to one group.

The similarity calculating unit 220 may calculate at least one of a Jacquard coefficient and a cosine coefficient for a user group including the recommendation target 110 and the plurality of information providers 120 to obtain the recommendation target 110 and the plurality of information providers 120 can be calculated. According to an embodiment of the present invention, the similarity calculating unit 220 may calculate at least one of the jacquard coefficient and the cosine coefficient according to which group of the plurality of groups the recommending target person 110 belongs to calculate the similarity can do. For example, when the recommended target 110 belongs to the first group, the similarity calculating unit 220 may calculate the Jacquard coefficients to calculate the similarity between the recommended target 110 and the plurality of information providers 120 . When the recommended target person 110 belongs to the second group, the similarity calculating unit 220 may calculate the jacquard coefficient and the cosine coefficient to calculate the similarity between the recommended target 110 and the plurality of information providers 120 have. The similarity calculating unit 220 may calculate the similarity by determining the weights of the Jacquard coefficients and the cosine coefficients according to the number of groups that classify the user group. When the recommended target person 110 belongs to the third group, the similarity calculating unit 220 may calculate the cosine coefficient to calculate the similarity between the recommended target 110 and the plurality of information providers 120. [

In addition, the similarity calculating unit 220 can determine a top K name having a high degree of similarity with the recommended target 110 among the plurality of information providers 120, based on the calculated similarity.

The prediction preference calculating unit 230 calculates the predicted preference of the recommending target person 110 for the at least one subject based on the calculated similarity and the evaluation scores of the plurality of information providers 120 for at least one subject can do. According to one embodiment of the present invention, the prediction preference calculating unit 230 calculates a predicted preference value for at least one subject based on the degree of similarity with the top K person having a high degree of similarity to the recommended person 110 and the evaluation score for the K subjects The predicted preference of the recommended target person 110 can be calculated. In calculating the prediction preference, the prediction preference calculating unit 230 may calculate an average value of the evaluation scores of the recommendation target 110 with respect to the subject in which the recommended observer 110 took the course, And an average value of the evaluation scores of the plurality of information providers 120 with respect to the user. The process of calculating the prediction preference of the recommended subject 110 by the prediction preference calculating unit 230 has been described in detail with reference to Equation (4).

The determination unit 240 can determine the top N class subjects having a high prediction preference among a plurality of subject subjects as the recommended subject subjects. The providing unit 250 may provide the recommended subject 110 to the recommended subject 110. For example, the providing unit 250 may transmit a recommendation course list to the terminal of the recommendation target 110.

3 is a flowchart illustrating a method of recommending a personalized course according to an embodiment of the present invention. The method of recommending a personalized course according to the embodiment shown in FIG. 3 includes steps that are processed in a time-series manner in the personalized course recommendation apparatus shown in FIG. Therefore, the contents described above with respect to the personalized course recommender shown in FIG. 1 can be applied to the personalized course recommendation method according to the embodiment shown in FIG.

In step S300, the personalized course recommender 130 acquires a plurality of information providers 120 based on at least any one of the number of the courses attended by the plurality of information providers 120 or the grade of the plurality of information providers 120 The user group including the user can be classified into a plurality of groups.

In step S310, the personalized course recommender 130 can determine the affiliation group of the recommendation target 110 based on at least one of the number of the subjects or the grade of the recommended subject 110.

In step S320, the personalized course recommender 130 can calculate the degree of similarity between the recommendation target 110 and the plurality of information providers 120. [ The personalized course recommender 130 may calculate at least one of the Jacquard coefficients and the cosine coefficients according to the group to which the recommendation target 110 belongs to calculate the degree of similarity with the plurality of information providers 120. [

In step S330, the personalized course recommender 130 can determine the top K of the plurality of information providers 120 with high similarity. In step S340, the personalized course recommender 130 acquires the degree of similarity between the recommendation target 110 and the K, the evaluation score of K information providers 120 on the subject, It is possible to calculate the predicted preference of the recommending target person 110 for at least one subject based on the evaluation score, the evaluation score for the subject in which the K information providers 120 have taken courses, and the like.

In step S350, the personalized course recommendation apparatus 130 can determine the top N recommended school subjects having a high prediction preference. In addition, in step S360, the personalized course recommender 130 may provide the determined N recommendable subjects to the recommendation target 110. [

Each of the above-described methods may also be embodied in the form of a recording medium including instructions executable by a computer, such as program modules, being executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium can include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes any information delivery media, including computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism.

It will be understood by those of ordinary skill in the art that the foregoing description of the embodiments is for illustrative purposes and that those skilled in the art can easily modify the invention without departing from the spirit or essential characteristics thereof. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed in a similar fashion may also be implemented.

The scope of the present invention is defined by the appended claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included within the scope of the present invention.

100: Personalized recommendation system
110: Recommended audience
120: plural information providers
130, 200: Personalized customized course recommendation device
210:
220:
230: Prediction Preference Calculator
240:
250: Offering

Claims (18)

In a personalized course recommendation method,
Calculating at least one of a Jaccard coefficient and a cosine coefficient for a user group including a recommended person and a plurality of information providers to calculate a degree of similarity between the recommended person and the plurality of information providers;
Calculating a predicted preference of the recommending target person for the at least one subject based on the calculated degree of similarity and the evaluation score of the plurality of information providers with respect to at least one subject;
Determining top N high school subjects having the high prediction preference as recommended subject subjects; And
Providing the recommendation subject to the recommended subject,
And a method for recommending a personalized course. The method according to claim 1,
Classifying the user group into a plurality of groups based on at least any one of the number of courses taken by the plurality of information providers or the grade of the plurality of information providers before the step of calculating the degree of similarity,
A method for recommending a personalized course. 3. The method of claim 2,
Determining whether the recommended target person belongs to any one of the plurality of groups based on at least any one of the number of classes or the grade of the recommended target person;
A method for recommending a personalized course. The method of claim 3,
Wherein the step of calculating the degree of similarity comprises calculating the degree of similarity by selecting at least one of the jacquard coefficient and the cosine coefficient according to which group of the plurality of groups the recommended person belongs to, Way. 5. The method of claim 4,
Wherein the step of calculating the degree of similarity is performed by determining weights of the Jacquard coefficients and the cosine coefficients according to the number of the plurality of groups. 3. The method of claim 2,
Wherein classifying the user group comprises:
Among the plurality of information providers, an information provider having a number of courses taken less than or equal to a preset first threshold value into a first group, and an information provider whose number of courses taken exceeds the first threshold value and is equal to or less than a second threshold value Classifying the information providers classified into the second group and having the number of the taken courses exceeding the second threshold value into the third group,
A method for recommending a personalized course. The method according to claim 6,
Determining whether the recommendation target person belongs to any one of the first group to the third group based on at least any one of the number of classes or the grade of the recommendation target person;
A method for recommending a personalized course. 8. The method of claim 7,
The step of calculating the degree of similarity may include:
Calculating the similarity between the recommendation target person and the plurality of information providers by calculating the jacquard coefficient when the recommended target person belongs to the first group,
Calculating the similarity between the recommendation target person and the plurality of information providers by calculating the jacquard coefficient and the cosine coefficient when the recommendation target belongs to the second group,
Wherein the degree of similarity between the recommended person and the plurality of information providers is calculated by calculating the cosine coefficient when the recommended person belongs to the third group. 9. The method of claim 8,
Wherein the step of calculating the predicted preference includes calculating an average value of the evaluation scores of the recommendation target person for the subject taken by the recommendation target person and an average value of the evaluation scores of the plurality of information providers with respect to the subjects taken by the plurality of information providers Wherein the learning is performed based on any one of the plurality of classes. The method according to claim 1,
Wherein said Jacquard coefficients are calculated based on attribution information of said recommended person and said plurality of information providers,
Wherein the attribute information includes at least one of a subject, an area of interest, a field of hobby, and a major that the recommended person and the plurality of information providers have preferred or have excellent grades. The method according to claim 1,
Determining a top K name having a high degree of similarity with the recommended target person among the plurality of information providers;
Further comprising:
Wherein the step of calculating the prediction preference is performed on the basis of the degree of similarity between the recommendation target person and the K person and the evaluation score of the K person. In a personalized course recommendation apparatus,
A similarity calculating unit for calculating at least one of a Jacquard coefficient and a cosine coefficient for a user group including a recommended person and a plurality of information providers to calculate a degree of similarity between the recommended person and the plurality of information providers;
A predicted preference calculating unit for calculating a predicted preference of the recommended person for the at least one subject based on the calculated degree of similarity and the score of the plurality of information providers for at least one subject;
A decision unit for deciding the top N classes of the predicted preference as the recommended course; And
A providing unit for providing the recommended subject to the recommended subject;
And a personalized recommendation system for recommending a course. 13. The method of claim 12,
A classifier for classifying the user groups into a plurality of groups based on at least any one of the number of courses taken by the plurality of information providers or the grade of the plurality of information providers,
Wherein the recommendation apparatus further comprises: 14. The method of claim 13,
Wherein the classifying unit determines whether the recommending target person belongs to any one of the plurality of groups based on at least any one of the number of classes or the grade of the recommended subjects,
Wherein the degree of similarity calculation unit calculates at least one of the Jacquard coefficient and the cosine coefficient according to which group of the plurality of groups the recommended person belongs to and calculates the degree of similarity. 14. The method of claim 13,
Wherein,
Among the plurality of information providers, an information provider having a number of courses taken less than or equal to a preset first threshold value into a first group, and an information provider whose number of courses taken exceeds the first threshold value and is equal to or less than a second threshold value Classifying the information providers classified into the second group and the information providers whose number of the taken courses exceeds the second threshold value into a third group. 16. The method of claim 15,
Wherein the classifying unit determines whether the recommended target person belongs to any one of the first group to the third group based on at least any one of the number of classes taken in the recommended subjects,
Wherein the similarity-
Calculating the similarity between the recommendation target person and the plurality of information providers by calculating the jacquard coefficient when the recommended target person belongs to the first group,
Calculating the similarity between the recommendation target person and the plurality of information providers by calculating the jacquard coefficient and the cosine coefficient when the recommendation target belongs to the second group,
Wherein the degree of similarity between the recommended person and the plurality of information providers is calculated by calculating the cosine coefficient when the recommended person belongs to the third group. 13. The method of claim 12,
Wherein the similarity calculating unit determines a top K name having a high degree of similarity with the recommending target person among the plurality of information providers,
Wherein the predictive preference calculating unit calculates the predictive preference of the recommendable target person for the at least one subject based on the degree of similarity between the recommended target person and the K name and the evaluation score of the K persons.  A computer-readable recording medium storing a program for causing a computer to execute the method according to any one of claims 1 to 11.
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