CN112015987A - Potential customer recommendation system and method based on enterprise tags - Google Patents
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Abstract
The invention discloses a potential customer recommendation system and a potential customer recommendation method based on enterprise tags, wherein the system comprises the following steps: the system comprises a client information management module, a client label management module, a client recommendation module and a client management module; the recommendation method comprises the steps of data crawling and storing, data cleaning, label extracting and cleaning, label printing by a user, graph data structure construction, calculation of the correlation degree of a client vertex relative to the user vertex and recommendation accuracy, and selection of a trained data model with high accuracy to be deployed on a server for client recommendation. The system and the method disclosed by the invention utilize a distributed big data processing technology to extract the enterprise tags more comprehensively, so that the enterprise is described more comprehensively by using the tags, the relation between the interest tags of the user and the enterprise tags is established more accurately, and the recommendation result is more accurate.
Description
Technical Field
The invention relates to the technical field of customer recommendation information, in particular to a potential customer recommendation system and a potential customer recommendation method based on enterprise tags.
Background
With the development of information technology and the internet, people gradually move from the times of lacking information to the times of information overload. In this age, people produce more and more data. But how to make these data work is a great challenge to mine information that users feel interesting.
In recent years, socio-economic speed is slowed down, and enterprises face greater and greater survival pressure, wherein one of the great problems faced by the enterprises is the problem of marketing and customer acquisition. Mining potential customers of an enterprise is an urgent need of current enterprises.
From the technical level, the enterprises are expected to be helped to develop the value of the data; from the development aspect, the enterprise is hoped to be helped to reduce the customer acquisition cost, and the power enterprise breaks through the development bottleneck.
Disclosure of Invention
In order to solve the technical problem, the invention provides a potential customer recommendation system and a potential customer recommendation method based on enterprise tags, so as to achieve the purpose of more accurate recommendation results.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an enterprise tag-based potential customer recommendation system comprising:
a client information management module: the system is used for managing client information, including basic information, legal action, business condition, business risk, enterprise development, intellectual property and news bulletin of the client;
a client tag management module: calculating a common label of a client by using a TF-IDF method, and pushing the common label to a user, wherein the user corrects and supplements the label according to own experience;
a client recommendation module: calculating potential customers of the enterprise by using a graph-based recommendation algorithm, and pushing the potential customers to users;
a client management module: and the users carry out marketing tracking on the clients, marketing tracking results are recorded in client management, the accuracy of recommendation is verified through the marketing tracking results, and the tag system and the recommendation algorithm are reversely optimized.
A potential customer recommendation method based on enterprise labels comprises the following steps:
(1) crawling and storing data: the method comprises the steps that a distributed acquisition technology is adopted to regularly acquire website information of enterprises and store data;
(2) data cleaning: unifying data formats of different data sources;
(3) extracting and cleaning labels: extracting tags from the cleaned data by using a text mining technology, and removing meaningless tags;
(4) labeling by a user: designating one collected enterprise as a user u and the rest as a client k, and enabling the user u to label the client k, wherein the label is selected from the cleaned labels obtained in the step (3), and the user label behavior that the user u labels a to the client k is represented by (u, k, a);
(5) construct graph data structure: randomly dividing a data set into a training set and a testing set, representing the label behaviors of users in the training set on a bipartite graph, and defining three different vertexes: adding three edges to the graph, adding one edge between a user vertex v (u) and a client vertex v (k) corresponding to the user u, adding one edge between the user vertex v (u) and a client vertex v (k), adding one edge between the user vertex v (u) and a label vertex v (a), adding one edge between the client vertex v (k) and the label v (a), and adding 1 to the weight of the edge if the two vertices are connected by the edge;
(6) calculating the relevance of the client vertex relative to the user vertex:
and calculating the correlation of all client vertexes relative to the user vertexes by adopting a modified Personalrank algorithm, wherein the iterative formula of the Personalrank is as follows:
wherein PR (u) is the correlation of all customer vertices with respect to u, d is the probability of continuing random walks, in (u) is the set of all customers pointing to u, PR (k) is the correlation of all customer vertices with respect to k, and out (k) is the set of customer vertices in the graph with links to k;
definition of riThe following were used:
riis the probability of firing of vertex u;
(7) calculation of recommendation accuracy:
selecting customers with high relevance to the user u as a set R (u), wherein T (u) is a set of customers which are actually labeled by the user u in the test set, and evaluating the accuracy of the personalized recommendation algorithm by using Precision and Recall:
(8) and selecting the trained data model with high accuracy to be deployed on the server for customer recommendation.
In the scheme, the data information of the enterprises collected and stored in the step (1) comprises basic information of the enterprises, legal action, business conditions, business risks, enterprise development, intellectual property rights and news bulletins.
In the scheme, in the step (1), the data of the enterprise is divided into structural data and non-structural data, the structural data is stored by adopting an hbase distributed file system, and the non-structural data is stored in an hdfs distributed system.
In the above scheme, in the step (2), the data cleaning method includes the following steps:
(1) removing the html label;
(2) removing special characters;
(3) detecting and merging repeated records;
(4) unifying data of different formats of different data sources into one.
In the scheme, in the step (3), the structural data is directly stored in the hbase, or is stored in the hbase after batch calculation in spark environment; for unstructured data, the tags are extracted using text mining techniques.
In the above scheme, in step (3), the label cleaning method includes:
(1) removing stop words with high word frequency;
(2) removing synonyms caused by different root words;
(3) synonyms due to separators are removed.
In the scheme, in the step (5), the data set is randomly divided into a training set and a testing set according to a ratio of 9:1, and the divided key values are users and clients and do not contain labels.
Through the technical scheme, the potential customer recommendation system and the recommendation method based on the enterprise tags acquire and clean multi-dimensional data related to enterprises in real time, tag the enterprise data, analyze the data of the clients and the clients interested in the transaction of the users, establish the relation between the interests of the users and the clients through the tags, and recommend the enterprises which are possibly interested by the users to the users so as to reduce the customer acquisition cost of the users. The invention utilizes the distributed big data processing technology to extract the enterprise tags more comprehensively, thereby describing the enterprises more comprehensively by the tags and further establishing the relation between the interest tags of the users and the enterprise tags more accurately.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a schematic flow chart of a potential customer recommendation method based on enterprise tags according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a potential customer recommendation system based on enterprise tags, which comprises:
a client information management module: the system is used for managing client information, including basic information, legal action, business condition, business risk, enterprise development, intellectual property and news bulletin of the client;
a client tag management module: calculating a common label of a client by using a TF-IDF method, and pushing the common label to a user, wherein the user corrects and supplements the label according to own experience;
a client recommendation module: calculating potential customers of the enterprise by using a graph-based recommendation algorithm, and pushing the potential customers to users;
a client management module: and the users carry out marketing tracking on the clients, marketing tracking results are recorded in client management, the accuracy of recommendation is verified through the marketing tracking results, and the tag system and the recommendation algorithm are reversely optimized.
A potential customer recommendation method based on enterprise tags, as shown in fig. 1, includes the following steps:
firstly, crawling and storing data:
regularly acquiring website information of enterprises every day by adopting a distributed acquisition technology, and storing data;
the collected and stored data information of the enterprise comprises basic information, legal action, business condition, business risk, enterprise development, intellectual property and news bulletin of the enterprise.
The data of the enterprise are divided into structural data and non-structural data, the structural data are stored by adopting an hbase distributed file system, and the non-structural data are stored in an hdfs distributed system.
Secondly, data cleaning:
the data collected from the website are very disordered, a large number of webpage labels and irrelevant data are collected and need to be cleaned, and the data formats of different data sources are greatly different and need to be unified. The cleaning and sorting module utilizing the data cleans regularly every day, and the method for cleaning the data comprises the following steps:
(1) removing the html label;
(2) removing special characters;
(3) detecting and merging repeated records;
(4) unifying data of different formats of different data sources into one.
Thirdly, extracting and cleaning labels:
with the help of business experts, the label of each data dimension is determined. For structural data, some fields can be used as tags and stored in hbase, and some fields need to be calculated in bulk in spark environment and then stored in hbase.
For non-structural line data, extracting tags by using a text mining technology, wherein the used text mining technology comprises the following steps:
data word segmentation, namely segmenting text type data into words by using a third-party word segmentation tool jieba, and then selecting medium labels from the words;
and (4) finding new words, and calculating the possibility of combining characters left and right into new words by using the information entropy.
The label cleaning method comprises the following steps:
(1) removing stop words with high word frequency;
(2) removing synonyms caused by different root words;
(3) synonyms due to separators are removed.
Fourthly, labeling by the user:
and (c) designating one collected enterprise as a user u, and the rest are clients k, so that the user u labels the clients k, the labels are selected from the cleaned labels obtained in the step three, and the user label behaviors that the user u labels the clients k with the labels a are represented by (u, k, a).
Fifthly, constructing a graph data structure:
and randomly dividing the data set into a training set and a testing set according to a ratio of 9:1, wherein the divided key values are users and clients and do not contain labels. That is, the user's multiple labels for the customer are grouped into either a training set or a test set.
Representing the label behaviors of users in the training set on a bipartite graph, and defining three different vertexes: adding three edges to the graph, adding one edge between a user vertex v (u) and a client vertex v (k) corresponding to the user u, adding one edge between the user vertex v (u) and the client vertex v (k), adding one edge between the user vertex v (u) and the label vertex v (a), adding one edge between the client vertex v (k) and the label v (a), and adding 1 to the weight of the edge if the two vertices have edge connection.
Sixthly, calculating the correlation degree of the client vertex relative to the user vertex:
and calculating the correlation of all client vertexes relative to the user vertexes by adopting a modified Personalrank algorithm, wherein the iterative formula of the Personalrank is as follows:
wherein PR (u) is the correlation of all customer vertices with respect to u, d is the probability of continuing random walks, in (u) is the set of all customers pointing to u, PR (k) is the correlation of all customer vertices with respect to k, and out (k) is the set of customer vertices in the graph with links to k;
definition of riThe following were used:
riis the firing probability of vertex u.
Using the above iterative formula, the correlation of all the customer vertices with respect to the user vertices can be calculated.
Seventhly, calculating the recommendation accuracy:
selecting the customers with high user vertex correlation corresponding to the user u as a set R (u), wherein T (u) is the set of the customers which are actually labeled by the user u in the test set, and evaluating the accuracy of the personalized recommendation algorithm by using accuracy Precision and Recall:
and eighthly, selecting the trained data model with high accuracy to be deployed on the server, and providing a model interface to carry out customer recommendation.
The recommendation based on the label has the greatest advantage that the label can be used for recommendation explanation, and the user can feel that the recommendation result is reasonable intuitively and difficultly.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An enterprise tag-based potential customer recommendation system, comprising:
a client information management module: the system is used for managing client information, including basic information, legal action, business condition, business risk, enterprise development, intellectual property and news bulletin of the client;
a client tag management module: calculating a common label of a client by using a TF-IDF method, and pushing the common label to a user, wherein the user corrects and supplements the label according to own experience;
a client recommendation module: calculating potential customers of the enterprise by using a graph-based recommendation algorithm, and pushing the potential customers to users;
a client management module: and the users carry out marketing tracking on the clients, marketing tracking results are recorded in client management, the accuracy of recommendation is verified through the marketing tracking results, and the tag system and the recommendation algorithm are reversely optimized.
2. A potential customer recommendation method based on enterprise labels is characterized by comprising the following steps:
(1) crawling and storing data: the method comprises the steps that a distributed acquisition technology is adopted to regularly acquire website information of enterprises and store data;
(2) data cleaning: unifying data formats of different data sources;
(3) extracting and cleaning labels: extracting tags from the cleaned data by using a text mining technology, and removing meaningless tags;
(4) labeling by a user: designating one collected enterprise as a user u and the rest as a client k, and enabling the user u to label the client k, wherein the label is selected from the cleaned labels obtained in the step (3), and the user label behavior that the user u labels a to the client k is represented by (u, k, a);
(5) construct graph data structure: randomly dividing a data set into a training set and a testing set, representing the label behaviors of users in the training set on a bipartite graph, and defining three different vertexes: adding three edges to the graph, adding one edge between a user vertex v (u) and a client vertex v (k) corresponding to the user u, adding one edge between the user vertex v (u) and a client vertex v (k), adding one edge between the user vertex v (u) and a label vertex v (a), adding one edge between the client vertex v (k) and the label v (a), and adding 1 to the weight of the edge if the two vertices are connected by the edge;
(6) calculating the relevance of the client vertex relative to the user vertex:
and calculating the correlation of all client vertexes relative to the user vertexes by adopting a modified Personalrank algorithm, wherein the iterative formula of the Personalrank is as follows:
wherein PR (u) is the correlation of all customer vertices with respect to u, d is the probability of continuing random walks, in (u) is the set of all customers pointing to u, PR (k) is the correlation of all customer vertices with respect to k, and out (k) is the set of customer vertices in the graph with links to k;
definition of riThe following were used:
riis the probability of firing of vertex u;
(7) calculation of recommendation accuracy:
selecting customers with high relevance to the user u as a set R (u), wherein T (u) is a set of customers which are actually labeled by the user u in the test set, and evaluating the accuracy of the personalized recommendation algorithm by using Precision and Recall:
(8) and selecting the trained data model with high accuracy to be deployed on the server for customer recommendation.
3. The method as claimed in claim 2, wherein the data information of the enterprise collected and stored in step (1) includes basic information of the enterprise, legal action, business situation, business risk, enterprise development, intellectual property and news bulletin.
4. The method for recommending potential clients based on enterprise tags as claimed in claim 2, wherein in step (1), the data of the enterprise is divided into structural data and non-structural data, the structural data is stored by using hbase distributed file system, and the non-structural data is stored in hdfs distributed system.
5. The enterprise tag-based potential customer recommendation method according to claim 2, wherein in the step (2), the data cleansing method comprises the following steps:
(1) removing the html label;
(2) removing special characters;
(3) detecting and merging repeated records;
(4) unifying data of different formats of different data sources into one.
6. The enterprise tag-based potential customer recommendation method according to claim 2, wherein in the step (3), the structural data is directly stored in the hbase, or is stored in the hbase after batch computation in spark environment; for unstructured data, the tags are extracted using text mining techniques.
7. The enterprise tag-based potential customer recommendation method according to claim 2, wherein in the step (3), the tag cleaning method comprises:
(1) removing stop words with high word frequency;
(2) removing synonyms caused by different root words;
(3) synonyms due to separators are removed.
8. The method for recommending potential customers based on enterprise labels as claimed in claim 2, wherein in step (5), the data set is randomly divided into a training set and a testing set according to a ratio of 9:1, and the divided keys are users and customers and do not contain labels.
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| CN117312678A (en) * | 2023-10-24 | 2023-12-29 | 亿企查科技有限公司 | Intelligent recommendation method, equipment and storage medium for potential clients based on big data |
| CN117312678B (en) * | 2023-10-24 | 2024-04-05 | 亿企查科技有限公司 | Intelligent recommendation method, equipment and storage medium for potential clients based on big data |
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