Trade object recommendation system and method based on big data

Abstract

The invention belongs to the technical field of Internet, and particularly relates to a trade object recommendation system and method based on big data, and the system comprises a master control module which is used for controlling the system; the system comprises a registration and login module used for login of trade users, a data storage module used for collecting and storing trade basic data of the users and establishing historical trade data, a big data calling module used for calling past historical trade data of the users, a data analysis module used for analyzing the historical trade data of the users, and a data storage module used for storing the historical trade data of the users. The analysis module is used for analyzing the called historical trade data, the screening module is used for screening the analyzed historical trade data, the data arrangement module is used for arranging the screened recommended trade commodity information conforming to the user, and the sorting module is used for carrying out priority sorting on the arranged recommended trade commodity information and sending the sorted recommended trade commodity information to the user. And the data sending module is used for recommending the sorted priority commodity information to the user.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a trading partner recommendation system and method based on big data. Background Art

[0002] In the Internet age of information overload, the importance of information recommendation technology to the operation of Internet companies is self-evident. At present, various websites use recommendation systems in the background and have launched the two functions of "Today's Recommendation" and "Guess You Like". For example, various shopping websites, paid download music websites, video websites and various service websites, etc., all of them calculate the most suitable candidate products to recommend to the user based on the user's access characteristics, and then display these products to the user for selection. Due to the huge number of products and users, the recommendation system makes extensive use of cloud computing-based data mining. By mining user behavior and product information, various recommendation algorithms are used to calculate the recommendation list, so as to achieve the effect of showing different content to different users and improve the conversion rate of the page.

[0003] With the rapid development and application of Internet technology, online shopping has become popular and has become one of the indispensable consumption channels for Chinese netizens. However, the corresponding problem is that the number of goods is too large, so consumers need to spend a lot of time and energy to identify the goods they need, which has caused the problem of "information overload". With the continuous expansion of e-commerce and the rapid growth of the number and types of goods, how to select personalized goods that you want to buy from a large number of goods has become an urgent problem to be solved. To this end, we propose a trading object recommendation system and method based on big data. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a trading partner recommendation system and method based on big data, which solves the background problem.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A trading partner recommendation system based on big data, comprising: Master control module: the master control module is used for system control; Registration and login module, used for trade users to log in; The data storage module is used to collect and store the basic transaction data of users and establish Historical trade data; Big data retrieval module, used to retrieve the user's historical trade data; Data analysis module, used to analyze the retrieved historical trade data; A screening module is used to screen the analyzed historical trade data; A data sorting module is used to sort out the recommended trade commodity information that meets the needs of the user; A sorting module is used to prioritize the sorted recommended trade commodity information; The data sending module is used to recommend the sorted priority product information to the user.

[0006] Furthermore, the historical trade data includes user registration information, commodity transaction trade data, commodity untransacted trade data, and commodity click-through information.

[0007] Furthermore, the process of registering the login module is as follows: S1: Users need to register when using the system for the first time; S2: When a user registers the system, he / she needs to register his / her name, age, gender, ID card, mobile phone number, login account and password; S3: Login verification is required when logging in; S4: After successful verification, you can log in to the system using the login account and password set in S2.

[0008] Furthermore, a data cleaning unit is provided in the data storage module, and the data cleaning unit is used to clean the original product click browsing information, remove noise data, and process it into formatted data.

[0009] Furthermore, the big data retrieval module can obtain three-dimensional data based on the historical behaviors of users of the same age, the preferences of users of the same age, and the regions where users of the same age are located. It can also retrieve from various dimensions of online relevance, online similarity, offline relevance, offline similarity, and recent hot-selling brand categories.

[0010] Furthermore, the user's historical behavior dimension can recommend related or similar products based on the user's previous purchasing behavior or browsing behavior.

[0011] Furthermore, for new user behaviors or users with relatively few previous trading behaviors, the user's geographical dimension is used.

[0012] A trading partner recommendation method based on big data, the method comprising the following steps: Step 1: Pre-establish a storage database; Step 2: The big data retrieval module retrieves the historical trade data, and the big data retrieval module retrieves the user data of the same age, the same code number and the same region; Step 3: The data analysis module, data sorting module and sorting module prioritize and sort the product information that meets the user's preferences; Step 4: The data sending module recommends the sorted products to the user. Beneficial Effects

[0013] The present invention provides a trading partner recommendation system and method based on big data. Compared with the prior art, it has the following beneficial effects: 1. The trading object recommendation system and method based on big data can start to use the system after verification. Then the big data retrieval module is used to retrieve the user's previous historical trade data, the data analysis module is used to analyze the retrieved historical trade data, the screening module is used to filter the analyzed historical trade data, the data sorting module is used to sort the recommended trade commodity information that meets the user's needs, the sorting module is used to prioritize the sorted recommended trade commodity information, and the data sending module is used to recommend the sorted priority commodity information to the user. The data storage module can collect and store the user's basic transaction trade data and establish historical trade data.

[0014] 2. The trading partner recommendation system and method based on big data, if the user is a new user, the big data retrieval module can retrieve the historical behavior of users of the same age, the preferences of users of the same age, and the preferences based on the regions where users of the same age are located, thereby facilitating users to better select products. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of a trading partner recommendation system based on big data according to the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a technical solution: a trading partner recommendation system based on big data, comprising: Master control module: the master control module is used for system control; Registration and login module, used for trade users to log in; The data storage module is used to collect and store the basic transaction data of users and establish historical trade data; Big data retrieval module, used to retrieve the user's historical trade data; Data analysis module, used to analyze the retrieved historical trade data; A screening module is used to screen the analyzed historical trade data; A data sorting module is used to sort out the recommended trade commodity information that meets the needs of the user; A sorting module is used to prioritize the sorted recommended trade commodity information; The data sending module is used to recommend the sorted priority product information to the user.

[0018] Historical trade data includes user registration information, commodity transaction trade data, commodity untransacted trade data, and commodity click-through information.

[0019] The process of registering the login module is as follows: S1: Users need to register when using the system for the first time; S2: When a user registers the system, he / she needs to register his / her name, age, gender, ID card, mobile phone number, login account and password; S3: Login verification is required when logging in; S4: After successful verification, you can log in to the system using the login account and password set in S2.

[0020] The data storage module is provided with a data cleaning unit, which is used to clean the original commodity click browsing information, remove noise data, and process it into formatted data.

[0021] The big data retrieval module can obtain data in three dimensions based on the historical behaviors of users of the same age, the preferences of users of the same age, and the regions where users of the same age are located. It can also retrieve data from various dimensions such as online relevance, online similarity, offline relevance, offline similarity, and recent hot-selling brand categories.

[0022] The user's historical behavior dimension can recommend related or similar products based on the user's previous purchasing or browsing behavior.

[0023] For new user behaviors or users with relatively few previous trading behaviors, the regional dimension of the user is used for analysis.

[0024] A trading partner recommendation method based on big data, the method comprising the following steps: Step 1: Pre-establish a storage database; Step 2: The big data retrieval module retrieves the historical trade data, and the big data retrieval module retrieves the user data of the same age, the same code number and the same region; Step 3: The data analysis module, data sorting module and sorting module prioritize and sort the product information that meets the user's preferences; Step 4: The data sending module recommends the sorted products to the user.

[0025] When the system is working, the user first logs in to the system through the registration and login module. During the login process, the registration and login module will verify the identity of the user. After the verification, the user can start to use the system. Then the big data retrieval module is used to retrieve the user's previous historical trade data, the data analysis module is used to analyze the retrieved historical trade data, the screening module is used to screen the analyzed historical trade data, the data sorting module is used to sort the recommended trade commodity information that meets the user's needs, the sorting module is used to prioritize the sorted recommended trade commodity information, and the data sending module is used to recommend the sorted priority commodity information to the user. Among them, the data storage module can collect and store the user's basic transaction trade data and establish historical trade data. The historical trade data includes the user's registration information, commodity transaction trade data, commodity untransacted trade data and commodity click browsing information. If the user is a new user, the big data retrieval module can retrieve the historical behavior of users of the same age, the preferences of users of the same age, and the preferences based on the region where users of the same age are located, so as to facilitate users to better choose commodities.

[0026] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0027] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A trading partner recommendation system based on big data, characterized in that: include: Master control module: the master control module is used for system control; Registration and login module, used for trade users to log in; The data storage module is used to collect and store the basic transaction data of users and establish Historical trade data; Big data retrieval module, used to retrieve the user's historical trade data; Data analysis module, used to analyze the retrieved historical trade data; A screening module is used to screen the analyzed historical trade data; A data sorting module is used to sort out the recommended trade commodity information that meets the needs of the user; A sorting module is used to prioritize the sorted recommended trade commodity information; The data sending module is used to recommend the sorted priority product information to the user.

2. The trading partner recommendation system based on big data according to claim 1, characterized in that: The historical trade data includes user registration information, commodity transaction trade data, commodity untransacted trade data, and commodity click-through information.

3. The trading partner recommendation system based on big data according to claim 2, characterized in that: The process of registering the login module is as follows: S1: Users need to register when using the system for the first time; S2: When a user registers the system, he / she needs to register his / her name, age, gender, ID card, mobile phone number, login account and password; S3: Login verification is required when logging in; S4: After successful verification, you can log in to the system using the login account and password set in S2.

4. The trading partner recommendation system based on big data according to claim 3 is characterized by: The data storage module is provided with a data cleaning unit, which is used to clean the original commodity click browsing information, remove noise data, and process it into formatted data.

5. The trading partner recommendation system and method based on big data according to claim 4, characterized in that: The big data retrieval module can obtain three-dimensional information based on the historical behaviors of users of the same age, the preferences of users of the same age, and the regions where users of the same age are located. It can also be retrieved from various dimensions such as online relevance, online similarity, offline relevance, offline similarity, and recent hot-selling brand categories.

6. The trading partner recommendation system based on big data according to claim 5, characterized in that: The user's historical behavior dimension can recommend related or similar products based on the user's previous purchasing behavior or browsing behavior.

7. The trading partner recommendation system based on big data according to claim 6, characterized in that: For new user behaviors or users with relatively few previous trading behaviors, the geographical dimension of the user is used.

8. A method for recommending trading partners based on big data according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: Step 1: Pre-establish a storage database; Step 2: The big data retrieval module retrieves the historical trade data, and the big data retrieval module retrieves the user data of the same age, the same code number and the same region; Step 3: The data analysis module, data sorting module and sorting module prioritize and sort the product information that meets the user's preferences; Step 4: The data sending module recommends the sorted products to the user.

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