KR102174717B1 - Product recommendation system and method based on user purchase criterion and product review - Google Patents

Abstract

According to an embodiment of the present invention, a product recommendation system based on user purchase criteria and product reviews includes: a data collection unit for collecting product list related to products searched by a user and product review data related to the products; A product evaluation unit that calculates an evaluation score for the product by digitizing the positive and negative evaluations contained in the product review data; And a product recommendation unit extracting a product to be recommended to the user from the product list based on the calculated evaluation score and providing the product to the user.

Description

Product recommendation system and method based on user purchase criteria and product review {PRODUCT RECOMMENDATION SYSTEM AND METHOD BASED ON USER PURCHASE CRITERION AND PRODUCT REVIEW}

Embodiments of the present invention relate to a product recommendation system and method. More specifically, product reviews are evaluated based on a purchase criterion selected by the user when purchasing a product, and products to be recommended to the user are sorted and provided to properly determine user preference. It is possible to recommend customized product information that has been reflected, and through this, the user purchase criteria and product review-based product recommendation system and method can be used to help users make efficient decisions by saving time required to purchase products. will be.

User reviews, which allow users to share user opinions on recent products, have been written by various buyers, and are being shared through various channels such as SNS and blogs. These product reviews contain more detailed and reliable information on the preferences of users about the product, and thus can be used usefully in a recommendation system, and many studies are actually being conducted.

In addition, due to the nature of the open web, the amount of product review data created by consumers online has rapidly increased, and various methods have been proposed to naturally analyze the vast amount of opinion information contained in product reviews.

However, the existing product recommendation system is based on information that is not objective or does not consider the user's preference, so the satisfaction level is low. Satisfaction with recommended products can be increased if the criteria that users consider important when purchasing products are considered and reflected.

As a related prior art, there is Korean Patent Publication No. 10-1621938 (name of invention: online shopping information recommendation system using user registration posting information, registration date: 2016.05.11).

An embodiment of the present invention can recommend customized product information that properly reflects the user's preference by evaluating product reviews based on the purchase criteria selected by the user when purchasing a product, and providing the products to be recommended to the user. Through this, a system and method for recommending products based on user purchase criteria and product reviews are provided to help users make efficient decisions by saving the time required to purchase products.

The problem to be solved by the present invention is not limited to the problem(s) mentioned above, and another problem(s) not mentioned will be clearly understood by those skilled in the art from the following description.

According to an embodiment of the present invention, a product recommendation system based on user purchase criteria and product reviews includes: a data collection unit for collecting product list related to products searched by a user and product review data related to the products; A product evaluation unit that calculates an evaluation score for the product by digitizing the positive and negative evaluations contained in the product review data; And a product recommendation unit extracting a product to be recommended to the user from the product list based on the calculated evaluation score and providing the product to the user.

The data collection unit collects product information including product IDs, product names, and URLs (Uniform Resource Locator) of each product included in the product list, and product review data for each product by crawling from the online shopping mall system. can do.

When crawling the product review data, the data collection unit may read and store sentences one by one based on a period to be useful for later morphological analysis.

When a purchase criterion for the product is selected by the user when searching for the product, the data collection unit may collect the product list and product review data based on the selected purchase criterion.

The data collection unit stores the collected data in a database (DB), and product information including an ID for each product, a product name, and a URL of each product included in the product list is stored in a first table of the database, and each Product IDs and product review data may be stored in a second table of the database, and product review data including the purchase criteria and a related word related to the purchase criteria may be stored in a third table of the database.

The data collection unit may perform noise filtering on the product review data to remove noise including special characters and meaningless stop words from the product review data.

The product evaluation unit may perform morpheme analysis on the product review data from which noise has been removed through the noise filtering, to quantify positive evaluation and negative evaluation contained in the product review data.

Based on the result of morphological analysis of the product review data by dividing each product included in the product list, the product evaluation unit counts the number of positive evaluations and negative evaluations among all product reviews for each product, and a positive evaluation ratio And calculating a negative evaluation ratio, calculating a positive score corresponding to the positive evaluation and a negative score corresponding to the negative evaluation based on the calculated positive evaluation ratio and negative evaluation ratio, and the calculated positive score and negative score. An average value of the evaluation score may be calculated by dividing the sum of the values by the number of all product reviews.

The product recommendation unit may extract the top N products (where N is a natural number) having a high average value of the evaluation score as products to be recommended to the user, and arrange the extracted products in ascending order and provide them to the user.

According to an embodiment of the present invention, a method for recommending products based on user purchase criteria and product reviews includes: collecting, by a data collection unit of the product recommendation system, a product list related to a product searched by a user and product review data related to the product; Calculating an evaluation score for the product by digitizing a positive evaluation and a negative evaluation included in the product review data by a product evaluation unit of the product recommendation system; And extracting a product to be recommended to the user from the product list based on the calculated evaluation score, and providing it to the user by a product recommendation unit of the product recommendation system.

In the method for recommending a product based on a user purchase criterion and a product review according to an embodiment of the present invention, when a purchase criterion for the product is selected by the user when searching for the product, the data collection unit is based on the selected purchase criterion. The method may further include collecting the product list and product review data.

The step of calculating the evaluation score for the product includes the number of positive evaluations and negative evaluations among all product reviews for each product based on the result of morphological analysis of the product review data by dividing each product included in the product list. Counting the number of and calculating a positive evaluation ratio and a negative evaluation ratio; Calculating a positive score corresponding to the positive evaluation and a negative score corresponding to the negative evaluation based on the calculated positive evaluation ratio and negative evaluation ratio; And calculating an average value of the evaluation score by dividing the sum of the calculated positive score and negative score by the number of all product reviews.

The providing to the user may include extracting the top N products (where N is a natural number) having a high average value of the evaluation score as products to be recommended to the user; And providing the extracted products to the user by sorting them in ascending order.

Details of other embodiments are included in the detailed description and accompanying drawings.

According to an embodiment of the present invention, when a product is purchased, product reviews are evaluated based on a purchase criterion selected by the user, and products to be recommended to the user are arranged and provided, so that user-customized product information that properly reflects the user's preference can be recommended. In this way, it is possible to help the user to make an efficient decision by saving the time it takes to purchase a product.

1 is a diagram illustrating a network configuration of a product recommendation system based on user purchase criteria and product review according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating a detailed configuration of the product recommendation system of FIG. 1.
3 is a block diagram illustrating a detailed configuration of the database DB of FIG. 2.
4 and 5 are flowcharts illustrating a method of recommending a product based on a user purchase criterion and a product review according to an embodiment of the present invention.
6 is a view showing a state in which a list of 12 products obtained by extracting only the names of products included in a review sentence in which a purchase criterion of'cost performance' and a keyword related to'price' is stored in a DB.
7 is an example of a review in which a morpheme analysis is performed by extracting only reviews including the keyword “price” associated with the cost performance, taking the purchase criterion of “cost performance” as an example.
8 is a diagram showing a table in which positive and negative scores are given for each of the previously extracted 12 product reviews, and an average is calculated.

Advantages and/or features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in a variety of different forms, only these embodiments make the disclosure of the present invention complete, and common knowledge in the technical field to which the present invention pertains. It is provided to completely inform the scope of the invention to those who have, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same elements throughout the specification.

In addition, a preferred embodiment of the present invention to be implemented below is already provided in each system functional configuration in order to efficiently describe the technical components constituting the present invention, or system functions commonly provided in the technical field to which the present invention belongs. The configuration will be omitted as much as possible, and a functional configuration that should be additionally provided for the present invention will be mainly described. If a person of ordinary skill in the art to which the present invention belongs will be able to easily understand the functions of components previously used among functional configurations that are not shown below and are omitted, the configuration omitted as described above. The relationship between the elements and the components added for the present invention will also be clearly understood.

In addition, in the following description, "transmission", "communication", "transmission", "receive" of a signal or information, and other terms with similar meanings refer to direct transmission of signals or information from one component to another Not only that, but it includes things that are transmitted through other components. In particular, "transmitting" or "transmitting" a signal or information to a component indicates the final destination of the signal or information and does not imply a direct destination. The same is true for "reception" of signals or information.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a network configuration of a product recommendation system based on user purchase criteria and product review according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating a detailed configuration of the product recommendation system of FIG. 3 is a block diagram illustrating a detailed configuration of the database DB of FIG. 2.

1 to 3, the product recommendation system 100 based on user purchase criteria and product reviews according to an embodiment of the present invention includes a data collection unit 210, a product evaluation unit 220, and a product recommendation unit 230. ), a database (DB) 240, and a control unit 250.

The data collection unit 210 may collect a product list related to a product searched by a user and product review data related to the product. Specifically, the user may access the online shopping mall system 102 through the user terminal 101 and search for a product to be purchased. In this case, the data collection unit 210 may interwork with the online shopping mall system 102 to collect a product list related to the searched product and product review data related to the product.

To this end, the data collection unit 210 stores product information including product IDs, product names, and URLs (Uniform Resource Locator) of each product included in the product list, and product review data for each product in the online shopping mall. It can be collected by crawling from system 102.

In addition, when the data collection unit 210 crawls the product review data, the sentences included in the product review data are read and stored one sentence at a time based on a period, thereby making it useful for later morpheme analysis. I can.

Meanwhile, the data collection unit 210 may collect the product list and the product review data based on the selected purchase criterion when a purchase criterion for the product is selected by the user when searching for the product. . Here, the purchase criterion is a criterion prioritized when the user selects a product, and may include durability, design, cost performance, etc. of the product.

For example, when the user selects the cost performance as a purchase criterion when searching for the product, the data collection unit 210 selects a review including a sentence related to a price among reviews related to the product, and the selected Reviews can be collected as the product review data.

In addition, the purchasing criteria may further include an influencer. Here, influencer is a collective term for “power bloggers” who operate blogs with great influence on portal sites, social network service (SNS) users with hundreds of thousands of followers, and single-person broadcasters. Influencers can be divided into creator-type influencers that produce content and model-type influencers in the fashion and beauty field. Meanwhile, a marketing method using a person exercising influence is influencer marketing, and the product recommendation system 100 based on user purchase criteria and product reviews according to an embodiment of the present invention can also be applied to influencer marketing.

For example, when the user selects an influencer as a purchase criterion when searching for the product, the data collection unit 210 may select a product related to the influencer among the products searched by the user, that is, a celebrity, a celebrity, or YouTube. Products used or recommended by influencers such as stars may be selected, and reviews related to the selected products may be collected as product review data. In this case, the data collection unit 210 may select a product related to the influencer from among the products searched by the user through data mining.

In this way, when searching for the product, the user may directly select a purchase criterion for the product, but in another embodiment, when searching for the product, the recommended purchase criterion may be applied automatically without selecting the purchase criterion for the product. have. For example, the data collection unit 210 calculates the number of user selections for each purchase criterion by analyzing the purchase criteria for a certain period (eg, one year), and based on the calculated number of user selections for each purchase criterion. Thus, by assigning a priority to each purchase criterion, the purchase criterion of the highest priority may be recommended to the user to aid in the user's selection, or the purchase criterion of the highest priority may be directly applied as a purchase criterion selected by the user.

For another example, the data collection unit 210 calculates the number of user selections for each purchase standard by analyzing the purchase criteria for a certain period (eg, one year), and calculates the number of user selections for each purchase criteria. Based on the weighting of each purchase criterion, the purchase criterion with the highest weight is recommended to the user, but if the weight of the purchase criterion (A) with the highest weight and the weight of the purchase criterion (B) other than the set value is less than the set value, the purchase criterion By recommending both (A) and purchase criteria (B) to the user, the purchase criteria preference list (A, B) may be provided to the user to directly select the purchase criteria preferred by other users.

For another example, the data collection unit 210 calculates the number of user selections for each purchase criterion according to gender and age-specific conditions by analyzing the purchase criteria for a certain period (eg, one year) based on the conditions for each gender and age, and , Based on the calculated number of user selections for each purchase criterion, a weight may be assigned to each purchase criterion.

The data collection unit 210 may store the collected data (product list, product review data) in the database (DB) 240. To this end, the database 240 may include a first table 310, a second data 320, and a third data 330 as shown in FIG. 3.

In other words, the data collection unit 210 stores product information including IDs for each product, product name, and URL of each product included in the product list in the first table 310 of the database 240, and Product IDs and product review data may be stored in the second table 320 of the database 240. Further, the data collection unit 210 may store product review data including the purchase criteria and a related word related to the purchase criteria in the third table 330 of the database 240.

The data collection unit 210 may perform noise filtering on the product review data to remove noise such as special characters and meaningless stop words from the product review data. Accordingly, the data collection unit 210 may reconstruct the product review data only with meaningful data to be useful for later morphological analysis.

The product evaluation unit 220 may calculate an evaluation score for the product by digitizing the positive evaluation and the negative evaluation included in the product review data. To this end, the product evaluation unit 220 performs a morpheme analysis on the product review data from which noise has been removed through the noise filtering, and quantifies the positive and negative evaluations contained in the product review data. You can calculate the evaluation score for.

Specifically, the product evaluation unit 220 morphemely analyzes the product review data by dividing each product included in the product list, and based on the result of the morphological analysis, the product evaluation unit 220 performs a positive evaluation among all product reviews for each product. The number and the number of negative evaluations can be counted. The product evaluation unit 220 calculates a positive evaluation ratio based on the number of positive evaluations relative to the number of total product reviews for each product, and negative based on the number of negative evaluations relative to the number of all product reviews for each product. You can calculate the evaluation rate.

The product evaluation unit 220 may calculate a positive score corresponding to the positive evaluation based on the calculated positive evaluation ratio, and calculate a negative score corresponding to the negative evaluation based on the calculated negative evaluation ratio. have. The product evaluation unit 220 may calculate an average value of evaluation scores for the product by performing a division operation that divides the sum of the calculated positive and negative scores by the number of product reviews.

The product recommendation unit 230 extracts a product to be recommended to the user from the product list based on the evaluation score (average value) calculated by the product evaluation unit 220, and uses the extracted product into the user terminal ( 101), it is possible to provide the user with a recommended product based on purchase criteria and product reviews.

At this time, the product recommendation unit 230 extracts the top N (the N is a natural number), for example, 10 products with a high average value of the evaluation score as products to be recommended to the user, and sorts the extracted products in ascending order. This can be provided to the user.

The database 240 may store data collected by the data collection unit 210. In particular, the database 240 may distribute and store the collected data in the first to third tables 310, 320, and 330 as described above.

Specifically, the first table 310 stores product information including an ID for each product, a product name, and a URL of each product included in the product list, and the second table 320 stores an ID for each product and the product. Review data can be stored. In addition, the third table 330 may store product review data including a purchase criterion selected by the user when searching for a product and a related word related to the purchase criterion.

The control unit 250 is a product recommendation system 100 based on user purchase criteria and product review according to an embodiment of the present invention, that is, the data collection unit 210, the product evaluation unit 220, and the product recommendation unit ( 230), the operation of the database 240 and the like can be controlled overall.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It can be implemented using one or more general purpose computers or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

4 and 5 are flowcharts illustrating a method of recommending a product based on a user purchase criterion and a product review according to an embodiment of the present invention.

The product recommendation method described here is only one embodiment of the present invention, and various steps may be added as needed, and the following steps may also be implemented by changing the order, so that the present invention will be described below. It is not limited to each step and the order to be described.

First, referring to FIGS. 1, 2 and 4, in step 410, the user may access the online shopping mall system 102 through the user terminal 101 and search for a product to be purchased.

Next, in step 420, the user may select a purchase criterion for the product when searching for the product. Here, the purchase criterion is a criterion prioritized when the user selects a product, and may include durability, design, cost performance, etc. of the product.

Next, in step 430, the data collection unit 210 of the product recommendation system 100 may collect a product list related to the product searched by the user and product review data related to the product.

Next, in step 440, the data collection unit 210 of the product recommendation system 100 may store the collected data (product list, product review data, etc.) in the database 240.

Next, in step 450, the product evaluation unit 220 of the product recommendation system 100 may calculate an evaluation score for the product by digitizing the positive and negative evaluations contained in the product review data. . This will be described in detail with reference to FIG. 5 as follows.

That is, in step 510, the product evaluation unit 220 may morphologically analyze the product review data by dividing each product included in the product list. Thereafter, in step 520, the product evaluation unit 220 counts the number of positive evaluations and negative evaluations among all product reviews for each product, based on the result of the morphological analysis, and the positive evaluation ratio and negative evaluation. You can calculate the ratio. Thereafter, in step 530, the product evaluation unit 220 may calculate a positive score corresponding to the positive evaluation and a negative score corresponding to the negative evaluation based on the calculated positive evaluation ratio and negative evaluation ratio. . Thereafter, in step 540, the product evaluation unit 220 may calculate an average value of evaluation scores for the product by dividing the sum of the calculated positive and negative scores by the number of all product reviews.

Referring back to FIG. 5, in step 460, the product recommendation unit 230 of the product recommendation system 100 sends the product from the product list to the user based on the evaluation score calculated by the product evaluation unit 220. You can extract products to be recommended. In this case, the product recommendation unit 230 may extract the top N products (where N is a natural number) having a high average value of the evaluation score as products to be recommended to the user.

Next, in step 470, the product recommendation unit 230 of the product recommendation system 100 may arrange the extracted products in ascending order and provide them to the user through the user terminal 101.

Hereinafter, an example of implementing a product recommendation system based on user purchase criteria and product reviews according to an embodiment of the present invention will be described in detail.

System implementation

The system construction environment used Windows operating system-based CPU-Intel i5-4690, RAM-8G. The language used is Python, and the database was built using MySQL. In addition, the case where the user selected'cost performance' as the purchase criterion was implemented as an example.

The data collection site was selected as'Company G', which recorded the highest transaction amount in Korea and ranked first in consumer brand reputation. It collects product information and reviews written by users for products limited to the'stroller' of childcare products among the categories of company G, the largest shopping mall in Korea. Since the quality of the product reviews influences this experiment, the category was selected as a childcare product with a lot of relatively honest user reviews. In more detail, the product name, product URL, and product review of the product item'stroller' with the most reviews in the'childcare products' category item are collected using Beautifulsoup, a library of Python that can crawl web data.

However, for product reviews provided by the online shopping mall system to be crawled, the list of all reviews can be viewed only when an action on the web browser that allows you to turn the page is performed after the'More' button or a certain number of reviews are written. Therefore, Selenium, a web page testing tool that can be automatically executed when crawling these actions, is used. The Selenium package functions to run a web browser through the URL of the product in the Python environment. It crawls the product name, product classification, and review using the HTML tag name.

Among the product reviews that have been crawled and stored previously, only the reviews with the number of reviews exceeding 20 out of the reviews including'cost-effectiveness' and'price' are extracted and stored. As a result of extraction as in FIG. 6, it was briefly divided into 12 products. 6 shows a state in which 12 product lists obtained by extracting only the names of products included in the review sentence in which the purchase criterion of'cost performance' and the keyword'price' related thereto are stored in the DB.

For product reviews obtained by crawling, install'KoNLP', a package that handles Hangul in Python programs. In addition, the morpheme analysis is performed based on the little dictionary to extract only nouns and adjectives from each product review sentence. Part of speech, which can determine the characteristics of a product, is judged as a noun, and part of speech that can indicate the user's sensibility for the characteristic is an adjective. In this process, sentences that do not contain adjectives that can judge positive or negative among the sentences of the review are judged as noise and excluded from the analysis.

7 is an example of a review in which a morpheme analysis is performed by extracting only reviews including the keyword “price” associated with the cost performance, taking the purchase criterion of “cost performance” as an example.

In order to recommend a product, it is necessary to determine whether there are many product reviews evaluated positively or many product reviews evaluated negatively among the product reviews extracted previously. Therefore, the number of positive or negative evaluations among all product reviews is counted and the average is calculated, and the product with the best evaluation among the entire product list is recommended to the user.

If you calculate the number of product reviews that received positive evaluations, you can find out the percentage of product reviews that received positive evaluations. Therefore, the average is obtained by dividing the sum of the positive and negative scores by the total number of product reviews (C).

Neutral opinions were difficult to discriminate, so they were judged as noise and excluded, and overall product reviews were positive, but they were included in the rating exclusion list, which did not have a good evaluation of the sorting keyword'price' or'cost performance'.

8 is a table in which positive and negative scores of each of the 12 product reviews extracted previously are given, and an average is calculated.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CDROMs and DVDs, and magnetic-optical media such as floptical disks. A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

As described above, although the embodiments have been described by the limited embodiments and drawings, various modifications and variations are possible from the above description by those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims fall within the scope of the following claims.

100: product recommendation system
101: user terminal
102: online shopping mall system
210: data collection unit
220: product evaluation unit
230: product recommendation unit
240: DB
250: control unit
310: first table
320: second table
330: third table

Claims (13)

When searching for a product, the purchase of the highest priority by analyzing the purchase criteria for a certain period of time, calculating the number of user selections for each purchase criteria, and giving priority to each purchase criteria based on the calculated number of user selections for each purchase criteria. Data for recommending a criterion to a user, and collecting product review data on the product and a product list related to the product searched by the user based on the selected purchase criterion when the recommended purchase criterion is selected by the user Collection unit;
A product evaluation unit that calculates an evaluation score for the product by digitizing the positive and negative evaluations contained in the product review data; And
A product recommendation unit that extracts a product to be recommended to the user from the product list based on the calculated evaluation score and provides it to the user
Including,
The above purchase criteria are
Including the durability, design, cost-effectiveness, and influencer of the product,
The product evaluation unit
Based on the result of morphological analysis of the product review data by dividing each product included in the product list, the number of positive evaluations and negative evaluations among all product reviews for each product is counted, and the positive evaluation ratio and negative evaluation ratio Is calculated, based on the calculated positive evaluation ratio and negative evaluation ratio, a positive score corresponding to the positive evaluation and a negative score corresponding to the negative evaluation are calculated, and a sum of the calculated positive and negative scores is calculated. A product recommendation system based on user purchase criteria and product reviews, characterized in that the average value of the evaluation score is calculated by dividing by the total number of product reviews.
The method of claim 1,
The data collection unit
Characterized in that, the product information including the ID for each product, the product name, and the URL (Uniform Resource Locator) of each product included in the product list, and product review data for each product are collected by crawling from the online shopping mall system. A product recommendation system based on user purchase criteria and product reviews.
The method of claim 2,
The data collection unit
When crawling the product review data, a product recommendation system based on user purchase criteria and product review, characterized in that the sentence is read and stored one by one based on a period to be useful for later morphological analysis.
delete The method of claim 1,
The data collection unit
The collected data is stored in a database (DB), and product information including an ID for each product, a product name, and URL of each product included in the product list is stored in the first table of the database, and the ID for each product and the Product review data is stored in a second table of the database, and product review data including the purchase criteria and a related word related to the purchase criteria are stored in a third table of the database. Based product recommendation system.
The method of claim 1,
The data collection unit
A product recommendation system based on user purchase criteria and product review, characterized in that noise including special characters and meaningless stop words is removed from the product review data by performing noise filtering on the product review data.
The method of claim 6,
The product evaluation unit
A product based on user purchase criteria and product review, characterized in that morphological analysis is performed on the product review data from which noise has been removed through the noise filtering to quantify positive and negative evaluations contained in the product review data. Recommendation system.
delete The method of claim 1,
The product recommendation section
User purchase criteria, characterized in that the highest N (where N is a natural number) products having a high average value of the evaluation score are extracted as products to be recommended to the user, and the extracted products are sorted in ascending order and provided to the user. Product recommendation system based on product reviews.
In the product recommendation method using a product recommendation system based on user purchase criteria and product review,
Calculating, by the data collection unit of the product recommendation system, a purchase criterion for a predetermined period of time when searching for a product, and the number of user selections for each purchase criterion;
Recommending the highest priority purchase criteria to the user by giving priority to each purchase criteria based on the calculated number of user selections for each purchase criteria, by the data collection unit;
When the recommended purchase criterion is selected by the user, collecting, by the data collection unit, a product list related to the product searched by the user and product review data related to the product based on the selected purchase criterion;
Calculating an evaluation score for the product by digitizing a positive evaluation and a negative evaluation included in the product review data by a product evaluation unit of the product recommendation system; And
Extracting a product to be recommended to the user from the product list based on the calculated evaluation score by a product recommendation unit of the product recommendation system and providing the product to the user
Including,
The above purchase criteria are
Including the durability, design, cost-effectiveness, and influencer of the product,
The step of calculating the evaluation score for the product
Based on the result of morphological analysis of the product review data by dividing each product included in the product list, the number of positive evaluations and negative evaluations among all product reviews for each product is counted, and the positive evaluation ratio and negative evaluation ratio Calculating a;
Calculating a positive score corresponding to the positive evaluation and a negative score corresponding to the negative evaluation based on the calculated positive evaluation ratio and negative evaluation ratio; And
Calculating an average value of the evaluation score by dividing the sum of the calculated positive score and negative score by the number of all product reviews
User purchase criteria and product review-based product recommendation method comprising a.
delete delete The method of claim 10,
The step of providing to the user
Extracting the highest N (where N is a natural number) products having a high average value of the evaluation score as products to be recommended to the user; And
Sorting the extracted products in ascending order and providing them to the user
User purchase criteria and product review-based product recommendation method comprising a.

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