CN109767302B - Method and device for constructing big data accurate model - Google Patents
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Abstract
The invention relates to the technical field of big data analysis, in particular to a method and a device for constructing a big data accurate model, wherein the method comprises the following steps: a data acquisition step, which is to acquire basic information and online shopping data information of a user; the user online shopping model building method comprises the following steps: s100: generating purchasing preferences and economic capabilities of the user; s200: generating a demand list of a user; s300: generating a recommendation list of a user; s400: analyzing the intimacy and social relationship between the user and other users to generate a social relationship network of the user; s500: and adding commodities of the demand lists of other users to the recommendation list of the user. According to the big data accurate model construction method and device, an accurate user model can be provided for a user based on a social relationship network, and accurate marketing is achieved.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a method and a device for constructing a big data accurate model.
Background
In recent years, the information and globalization trends based on the internet and the mobile internet have deeply changed our life, production and competition modes. With the advent of the big data age, the demand for precision marketing is also rising. How to dig the deep level relation under the big data through the technical means makes the marketing more accurate and effective becomes the central importance in the marketing.
The user portrait, namely the labeling of user information, is an effective way to draw a target user, to contact the user with complaints and design directions, and the goal is to establish descriptive label attributes for the user in many dimensions. The method abstracts a user's complete picture to mine user's demand and analyze user's preference by collecting and analyzing data of information such as user's basic attribute, social attribute, living habits, consumption behaviors, etc., and further can mine potential customer groups to carry out targeted commodity recommendation, thus realizing accurate marketing.
In the current e-commerce industry, a method for constructing a user portrait is generally to store logs of behaviors of users in a station, such as access commodity categories and the like, then traverse all user behavior logs within a certain time window, and calculate the user behavior logs according to certain weight attenuation functions to obtain the current latest user portrait. In the existing component method of the user portrait model, each user is isolated in processing, the social relationship network of the user is not considered, the user portrait is incomplete, and the accuracy and the success rate of commodity recommendation are easily reduced.
Disclosure of Invention
The invention aims to provide a big data accurate model construction method, which can provide an accurate user model for a user based on a social relationship network and realize accurate marketing.
In order to solve the technical problem, the present application provides the following technical solutions:
the method for constructing the big data accurate model comprises the following steps:
a data acquisition step, which is to acquire basic information and online shopping data information of a user;
a user online shopping model establishing step, namely establishing a user online shopping model according to basic information and online shopping data information of a user, wherein the user online shopping model comprises a purchasing preference, a social relationship network, economic capacity, a demand list and a recommendation list; the online shopping data information comprises a purchase record, a browsing record, shopping cart information, payment information and online shopping account information;
the user online shopping model establishing step comprises the following steps:
s100: generating purchasing preference and economic capacity of the user according to the purchasing record and the browsing record of the user;
s200: generating a demand list of the user according to the purchasing preference, browsing record and shopping cart information of the user;
s300: generating a recommendation list of the user according to the demand list, purchasing preference and economic capacity of the user;
s400: analyzing the intimacy and social relationship between the user and other users according to the purchase record, the payment information and the online shopping account information of the user to generate a social relationship network of the user;
s500: and adding commodities of a demand list of users which are related to the users in a preset relationship type or have intimacy with the users exceeding a preset value into the recommendation list of the users.
In the technical scheme, the social relationship between the users is analyzed through S400 according to the purchasing behavior, the purchasing account number and other data of the users, so that the finally generated online purchasing model of the users comprises the social relationship network of the users, the final portrait model of the users can be more accurate, meanwhile, the commodity of a demand list of other users close to the users, such as relatives, spouses, friends and the like of the users is pushed to the users by utilizing the intimacy and the social relationship, the users can know the recent demand of the friends, on one hand, when the users select gifts for the relatives, spouses or friends, the commodities selected by the users can be needed by the other parties, the difficulty of the users in selecting the gifts is further reduced, and the real demand and the like of the selected gifts for the other parties are ensured; on the other hand, by the mode, accurate recommendation can be realized, and the recommendation success rate and the commodity transaction rate can be improved.
And further, a feedback adjustment step is included, wherein the feedback adjustment step comprises a social relationship updating step, whether the user purchases the required commodities of other users in the recommendation list is judged, and if yes, the intimacy between the two users is updated.
Whether the user purchases the recommended goods is used as feedback to adjust the intimacy between the users.
Further, the social relations comprise a friend relation, a couple relation and a family relation, and the preset relation types are the couple relation and the family relation. All the related personal relationships of the user in daily life can be basically covered through the three social relationships.
Further, S400 specifically includes:
screening online shopping records of recipients other than the user from the online shopping records of the user to obtain online shopping present records;
associating the phone number and the name of the receiver in the online shopping presentation record with the corresponding user;
judging the relationship between the two users according to the presented commodities and the left message information, and judging the intimacy of the two users according to the quantity of online shopping presentation records and the corresponding value;
according to the information of the user, searching corresponding payment record and judging the relationship and affinity of the two users according to the left message and the amount of the payment;
and analyzing the relationship and the affinity between the user and the friends thereof according to the friend relationship and the remark information of the online shopping account of the user.
Through the online shopping presentation record, the identity information of the recipient, the presented commodity content and the value can be determined, and then the relationship between the two users can be judged. The social relationship of the user is comprehensively analyzed and detected through three aspects of presentation records, payment information and account friend information.
Further, the goods added to the recommendation list of the user in S500 correspond to the economic capability of the user. The situation that the added recommended commodities exceed the economic capability range of the user is avoided, and further recommendation is more accurate.
Further, another object of the present invention is to provide an apparatus for constructing a big data precision model, the apparatus comprising:
the data acquisition module is used for acquiring basic information and online shopping data information of a user;
the user online shopping model establishing module is used for establishing a user online shopping model according to the basic information and the online shopping data information of the user, wherein the user online shopping model comprises a purchasing preference, a social relationship network, economic capacity, a demand list and a recommendation list; the online shopping data information comprises a purchase record, a browsing record, shopping cart information, payment information and online shopping account information;
the user online shopping model building module comprises:
the preference and economic capacity calculation module is used for generating the purchase preference and economic capacity of the user according to the purchase record and the browsing record of the user;
the demand generation module is used for generating a demand list of the user according to the purchasing preference, the browsing record and the shopping cart information of the user;
the recommendation generation module is used for generating a recommendation list of the user according to the demand list, the purchase preference and the economic capability of the user;
the social relationship analysis module is used for analyzing the intimacy and the social relationship between the user and other users according to the purchase record, the payment information and the online shopping account information of the user to generate a social relationship network of the user;
and the recommendation adjustment module is used for adding commodities of a demand list of users which are related to the users in a preset relationship type or the users with the affinity exceeding a preset value into the recommendation list of the users.
The social relationship analysis module is used for analyzing the intimacy and the social relationship among the users, and the recommendation adjustment module is used for recommending commodities for the users according to the social relationship, so that the users can know the recent requirements of friends of the users, on one hand, when the users select gifts for relatives, spouses or friends, the commodities selected by the users are required by the other party, the difficulty of the users in selecting the gifts is further reduced, and the real requirements and the likes of the selected gifts for the other party are ensured; on the other hand, by the mode, accurate recommendation can be realized, and the recommendation success rate and the commodity transaction rate can be improved.
And further, the system also comprises a feedback updating module which is used for judging whether the user purchases the required commodities of other users in the recommendation list or not, and if so, updating the intimacy between the two users. Through the feedback updating module, the intimacy between users is subjected to feedback adjustment, and the accuracy of the model is ensured.
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FIG. 1 is a flowchart illustrating a method for constructing a big data precision model according to an embodiment of the present invention;
fig. 2 is a logic block diagram of an embodiment of a big data precision model building apparatus according to the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
as shown in fig. 1, the method for constructing the big data precision model according to the embodiment is mainly applied to an e-commerce platform, provides more precise online shopping recommendation service for users, and each commodity in the e-commerce platform is provided with a preference tag, a value tag, a category tag and an affinity tag; the preference labels represent shopping preferences of users, such as labels of quality of view, cost performance, Chinese style and the like, the category labels represent specific types of commodities, such as mobile phones, computer accessories, winter clothes, summer clothes, jackets and the like, and are mainly used for similar recommendation, the value labels are mainly divided into value grades according to the ranking of the prices of the commodities in the similar commodities, the value grades are used as the value labels, in the application, the value labels are divided into ten grades according to the price from low to high, if a certain mobile phone has the price in the range of 40% -50% in all the mobile phones of the whole platform in the sequence from low to high, the value label corresponding to the mobile phone is 5 grades. The intimacy label is an attribute preset in advance according to the specific type and value of the commodity.
The method comprises the following steps:
the method comprises the steps of data acquisition, wherein basic information and online shopping data information of a user are acquired, the basic information comprises information such as the name, the mobile phone number, the age and the sex of the user, and the online shopping data information comprises purchase records, browsing records, shopping cart information, payment information and online shopping account information;
and a step of establishing a user online shopping model, which is to establish the user online shopping model according to the basic information and the online shopping data information of the user, wherein the user online shopping model comprises purchasing preference, a social relationship network, economic capacity, a demand list and a recommendation list.
The user online shopping model establishing step specifically comprises the following steps:
s100: generating purchasing preference and economic capacity of the user according to the purchasing record and the browsing record of the user;
s200: generating a demand list of the user according to the purchasing preference, browsing record and shopping cart information of the user;
s300: generating a recommendation list of the user according to the demand list, purchasing preference and economic capacity of the user;
s400: analyzing the intimacy and social relationship between the user and other users according to the purchase record, the payment information and the online shopping account information of the user to generate a social relationship network of the user;
s500: and adding commodities of a demand list of users which are related to the users in a preset relationship type or have intimacy with the users exceeding a preset value into the recommendation list of the users.
Specifically, in S100, the quantity statistics of preference tags and value tags is performed on the commodities corresponding to the online shopping records and the browsing records of the user, the preference tags with the first three quantities are selected as the purchasing preferences of the user, and the value tag with the largest quantity is selected as the economic capability of the user.
S200, specifically, screening out commodities which accord with the purchasing preference of the user from the browsing records of the user; combining the screened commodities with commodities in a shopping cart of a user to form a demand list; for example, the user pays more attention to the cost performance, and the latest browsing records of the user are related to the mobile phones, so that the mobile phones with higher cost performance are selected from the mobile phone commodities firstly and then the commodities in the shopping cart of the user are combined to form a user demand list.
S300, for each commodity in the demand list, finding other similar commodities according to the category label of the commodity, and screening the similar commodities according to the purchasing preference of the user to obtain a recommendation list; for example, if the demand list of the user includes a mobile phone and a charge pal, all the mobile phones and charge pal on the platform are screened according to the preference of the user and the attributes of the commodities of the current demand list, such as price interval, color, style and the like, so that the original demand list is expanded into a recommendation list.
S400 specifically comprises the following steps:
screening online shopping records of recipients other than the user from the online shopping records of the user to obtain online shopping present records;
associating the phone number and the name of the receiver in the online shopping presentation record with the corresponding user;
judging the relationship between the two users according to the presented commodities and the left message information, and judging the intimacy of the two users according to the quantity of online shopping presentation records and the corresponding value;
according to the information of the user, searching corresponding payment record and judging the relationship and affinity of the two users according to the left message and the amount of the payment;
and analyzing the relationship and the affinity between the user and the friends thereof according to the friend relationship and the remark information of the online shopping account of the user.
Through the online shopping presentation record, the identity information of the recipient, the presented commodity content and the value can be determined, and then the relationship between the two users can be judged. The social relationship of the users is comprehensively analyzed and detected through three aspects of presentation records, payment information and account and friend information, for example, the users present a bundle of roses to another user and leave a corresponding message, and the system can judge that the relationship between the two users is a lover relationship or a couple relationship according to the sent roses, the number of times of delivery and the message. For example, if a user purchases a certain product and pays another user for a fee instead, and the fee-replacement message includes a name, the relationship between the two persons can be estimated from the value of the paid-for product and the name appearing in the fee-replacement message.
The social relations comprise a friend relation, a lover relation and a family relation, the preset relation type is a lover relation and a family relation, in the embodiment, the S500 step is mainly used for recommending the commodity of the requirement list of the other party to family, lovers and friends with the intimacy exceeding the preset value, for example, if the family of the user wants to buy a handbag recently, the requirement can be recommended to the recommendation list of the user, the user is reminded and recommended, the user is prevented from not knowing what to buy and sending the commodity to the family, if the user purchases the recommended commodity, the requirement of the family is just met, and the gift is enabled to be more in line with the actual requirement of the gift recipient. In this embodiment, the commodities added to the recommendation list of the user correspond to the economic capability of the user. The situation that the added recommended commodities exceed the economic capability range of the user is avoided, and further recommendation is more accurate.
And the feedback adjustment step comprises a social relationship updating step, and is used for judging whether the user purchases the required commodities of other users in the recommendation list or not, and if so, updating the intimacy between the two users. And the social relationship updating step updates the intimacy between the two users according to the intimacy label. According to the intimacy label contained in the purchased commodity, the intimacy of the two persons is updated and adjusted, and then the social relationship between the users can be adjusted in real time.
As shown in fig. 2, the embodiment further discloses a device for constructing a big data precision model using the method for constructing a big data precision model, where the device includes:
the data acquisition module is used for acquiring basic information and online shopping data information of a user; the basic information comprises information such as the name, the mobile phone number, the age, the sex and the like of a user, and the online shopping data information comprises purchasing records, browsing records, shopping cart information, payment information and online shopping account information.
And the user online shopping model establishing module is used for establishing a user online shopping model according to the basic information and the online shopping data information of the user, and the user online shopping model comprises purchasing preference, a social relationship network, economic capacity, a demand list and a recommendation list.
The user online shopping model building module comprises:
and the preference and economic capacity calculation module is used for generating the purchase preference and economic capacity of the user according to the purchase record and the browsing record of the user.
And the demand generation module is used for generating a demand list of the user according to the purchasing preference, the browsing record and the shopping cart information of the user.
And the recommendation generation module is used for generating a recommendation list of the user according to the demand list, the purchase preference and the economic capability of the user.
And the social relationship analysis module is used for analyzing the intimacy and the social relationship between the user and other users according to the purchase record, the payment information and the online shopping account information of the user to generate a social relationship network of the user.
And the recommendation adjustment module is used for adding commodities of a demand list of users which are related to the users in a preset relationship type or the users with the affinity exceeding a preset value into the recommendation list of the users.
And the feedback updating module is used for judging whether the user purchases the required commodities of other users in the recommendation list or not, and if so, updating the intimacy between the two users. Through the feedback updating module, the intimacy between users is subjected to feedback adjustment, and the accuracy of the model is ensured.
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (6)
1. The method for constructing the big data accurate model is characterized by comprising the following steps: the method comprises the following steps:
a data acquisition step, which is to acquire basic information and online shopping data information of a user;
a user online shopping model establishing step, namely establishing a user online shopping model according to basic information and online shopping data information of a user, wherein the user online shopping model comprises a purchasing preference, a social relationship network, economic capacity, a demand list and a recommendation list; the online shopping data information comprises a purchase record, a browsing record, shopping cart information, payment information and online shopping account information;
the user online shopping model establishing step comprises the following steps:
s100: generating purchasing preference and economic capacity of the user according to the purchasing record and the browsing record of the user;
s200: generating a demand list of the user according to the purchasing preference, browsing record and shopping cart information of the user;
s300: generating a recommendation list of the user according to the demand list, purchasing preference and economic capacity of the user;
s400: analyzing the intimacy and social relationship between the user and other users according to the purchase record, the payment information and the online shopping account information of the user to generate a social relationship network of the user;
s400 specifically comprises the following steps:
screening online shopping records of recipients other than the user from the online shopping records of the user to obtain online shopping present records;
associating the phone number and the name of the receiver in the online shopping presentation record with the corresponding user;
judging the relationship between the two users according to the presented commodities and the left message information, and judging the intimacy of the two users according to the quantity of online shopping presentation records and the corresponding value;
according to the information of the user, searching corresponding payment record and judging the relationship and affinity of the two users according to the left message and the amount of the payment;
analyzing the relationship and the affinity between the user and friends thereof according to the friend relationship and the remark information of the online shopping account of the user;
s500: and adding commodities of a demand list of users which are related to the users in a preset relationship type or have intimacy with the users exceeding a preset value into the recommendation list of the users.
2. The big data accurate model building method according to claim 1, wherein: and the feedback adjustment step comprises a social relationship updating step, and is used for judging whether the user purchases the required commodities of other users in the recommendation list or not, and if so, updating the intimacy between the two users.
3. The big data precision model building method according to claim 2, wherein: the social relations comprise a friend relation, a couple relation and a family relation, and the preset relation types are the couple relation and the family relation.
4. The big data precision model building method according to claim 3, wherein: the goods added to the recommendation list of the user in S500 correspond to the economic capability of the user.
5. The utility model provides a device for constructing big accurate data model which characterized in that: the device includes:
the data acquisition module is used for acquiring basic information and online shopping data information of a user;
the user online shopping model establishing module is used for establishing a user online shopping model according to the basic information and the online shopping data information of the user, wherein the user online shopping model comprises a purchasing preference, a social relationship network, economic capacity, a demand list and a recommendation list; the online shopping data information comprises a purchase record, a browsing record, shopping cart information, payment information and online shopping account information;
the user online shopping model building module comprises:
the preference and economic capacity calculation module is used for generating the purchase preference and economic capacity of the user according to the purchase record and the browsing record of the user;
the demand generation module is used for generating a demand list of the user according to the purchasing preference, the browsing record and the shopping cart information of the user;
the recommendation generation module is used for generating a recommendation list of the user according to the demand list, the purchase preference and the economic capability of the user;
the social relationship analysis module is used for analyzing the intimacy and the social relationship between the user and other users according to the purchase record, the payment information and the online shopping account information of the user to generate a social relationship network of the user;
and the recommendation adjustment module is used for adding commodities of a demand list of users which are related to the users in a preset relationship type or the users with the affinity exceeding a preset value into the recommendation list of the users.
6. The big data precision model building device according to claim 5, wherein:
the system also comprises a feedback updating module which is used for judging whether the user purchases the demand commodities of other users in the recommendation list or not, and if so, updating the intimacy between the two users.
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| CN112288510A (en) * | 2020-08-25 | 2021-01-29 | 北京沃东天骏信息技术有限公司 | Article recommendation method, device, equipment and storage medium |
| CN113706251A (en) * | 2021-08-30 | 2021-11-26 | 平安国际智慧城市科技股份有限公司 | Commodity recommendation method and device based on model, computer equipment and storage medium |
| CN113643108B (en) * | 2021-10-15 | 2022-02-08 | 深圳我主良缘科技集团有限公司 | Social friend-making recommendation method based on feature recognition and analysis |
| CN114357280A (en) * | 2021-11-29 | 2022-04-15 | 泰康保险集团股份有限公司 | Information pushing method and device, electronic equipment and computer readable medium |
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