WO2018076695A1

WO2018076695A1 - Smart recommendation system and smart recommendation method

Info

Publication number: WO2018076695A1
Application number: PCT/CN2017/087060
Authority: WO
Inventors: 韩仁彬
Original assignee: 上海斐讯数据通信技术有限公司
Priority date: 2016-10-31
Filing date: 2017-06-02
Publication date: 2018-05-03
Also published as: CN106570722A; CN106570722B

Abstract

A smart recommendation system and a smart recommendation method, the method comprising the following steps: S200: acquiring basic information and behavioral activity information of a user; S300: storing the basic information and behavioral activity information of the user; S400: screening and classifying the mass of basic information and behavioral activity information of the user; and S500: sending to the user corresponding recommendation information.

Description

Intelligent recommendation system and intelligent recommendation method

Technical field

This application relates to the field of information technology applications, and more particularly to the field of advertising of the Internet/mobile Internet.

Background technique

The key factor to measure the pros and cons of a recommendation system is the accuracy of the recommendation. So the core technical question is how to extract the data, how to mine the user information to analyze its behavior characteristics to understand the user's preferences and potential preferences, and finally recommend the personality accordingly. . The present invention provides a solution to this technical problem from a new perspective.

Data mining is one of the hot topics in computer research today. Clustering analysis, as an unsupervised machine learning method, refers to how to automatically divide data objects into different clusters for a data object set, so that the same cluster Objects have a high degree of similarity under certain metrics, while data objects in different clusters have low similarities. Cluster analysis is widely used in the frontier fields of machine learning, data mining, speech recognition, image segmentation, business analysis and bioinformatics processing. At present, the traditional clustering algorithm mainly includes five categories: they are based on partitioning clustering algorithm, layer-based clustering algorithm, density-based clustering algorithm, grid-based clustering algorithm and model-based clustering. algorithm.

Among the clustering algorithms, the K-means algorithm is a partition-based clustering algorithm, which is simple and fast, and is known for its efficiency. However, the original K-means algorithm has some defects: 1) The original algorithm requires the user to give the K value, that is, the number of clusters. This value is mainly derived from experience, so it is difficult to determine the K value; 2) The algorithm is sensitive to the initial clustering center. The advantages and disadvantages of the initial center selection will affect the clustering result and affect the efficiency of the algorithm. 3) The algorithm is sensitive to abnormal data, which will cause the result to fall into the local optimal solution.

At present, some scholars have made some improvements to the initial center point problem. For example, to prevent the result from falling into local optimum, it is common to select a relatively scattered point that is far away as the initial center point. However, if only the distance factor is considered, it is easy to select an abnormal point, which in turn affects the clustering effect. Scholars have also considered these issues, and from the perspective of density, to filter out abnormal points. Another problem is that the initial center point may be selected as a point in the same cluster, that is, although the density of a certain point is relatively large, the corresponding cluster of the point is already selected as the center point. You should choose a representative point in other classes. Otherwise, it will also lead to the result that it is easy to fall into the local optimal solution.

The recommendation system refers to analyzing the user's behavior characteristics by recording the user's relevant historical data for intelligent recommendation, and is widely used in various fields such as e-commerce, content-based media, life service, and search. For example, an e-commerce website evaluates a user's browsing data and purchase records to judge their interests and economic ability to recommend products that they may like.

With the maturity of GPS and wireless cellular positioning technologies, more and more mobile smart device users have generated massive location data. The service based on location data is also becoming more and more abundant, which is related to our food, clothing, housing and transportation. The application scenarios include urban calculation (traffic flow analysis, optimal route planning, etc.), and nearby recommendations (restaurants, hotels). The location data records the user's historical location trajectory, which reflects the user's behavior patterns and living habits to some extent.

Summary of the invention

The data collected by the existing recommendation system is not comprehensive. Most of them collect relevant data from their own platforms for mining analysis. For example, the news site extracts and analyzes the user history browsing data, and the catering APP extracts and analyzes the user's catering records and related. . Relatively speaking, most current location data services only utilize the visualization of the trajectory data itself, that is, provide a wide range of services (catering, entertainment, travel) from the user's current location. The present invention deeply analyzes the potential value of the location data from the perspective of location data, and provides a complete set of solutions for the data mining to improve the recommendation system and improve the accuracy of the recommendation.

The present invention is directed to the above problems, and the present invention is achieved by the following technical solutions:

An intelligent recommendation method, the method comprising the following steps:

S200: Obtain basic information and behavior activity information of the user;

S400: screening and classifying a mass of the basic information and behavior activity information of the user;

S500: Send corresponding recommendation information to the user.

The basic information includes: gender, age, occupation, etc.;

The behavioral activity information includes: address information, consumption information, time information of staying, and the like.

Further, in the smart recommendation method, the step S200 includes: S210: input basic information of the user by using an input module of the smart mobile terminal;

S220: Acquire, by the navigation positioning module, the time recording module, the electronic payment module, or the electronic sign-in module of the smart mobile terminal, behavior activity information of the user.

Further, in the smart recommendation method, the step S400 includes: S410: determining, by the trajectory module of the network device, a latitude and longitude of a location of occurrence of a behavior (feature) of the user in different time periods of the day;

S420: The trajectory module determines an address name of a location where the behavior activity (feature) occurs according to the latitude and longitude query map database (for example, Baidu map, Gaode map, Google map, 360 map, or self-built map database, etc.) For example: XX Hotel, XX Playground, XX Sports Hall, XX Tea House, XX Bar, XX Bookstore, XX Cinema, XX Museum, XX Health Museum, XX Beauty Salon, XX Supermarket, XX Shopping Mall, XX Tourist Attractions).

Further, in the smart recommendation method, the step S500 includes at least one of the following steps:

S510: The recommendation module of the network device sends the corresponding recommendation information to the user, where the recommendation information is the behavior activity of the user that occurs in the same time period in the history record. Frequency-ordered behavioral activity (feature) related location information;

For example, during the annual holiday of May 1st and 11th, the system recommends travel information to users who have traveled during this time, and recommends hotel information to users at noon or evening.

S520: When the user is in a certain location, the recommendation module of the network device sends the corresponding recommendation information to the user, where the recommendation information is sorted by the frequency of the behavior activity of the user around the certain location. The behavior related activity information;

For example, if a user comes to a certain shopping mall, the system will recommend the product promotion discount information or the surrounding leisure and entertainment information of the shopping mall.

S530: The recommendation module of the network device sends the same recommendation information to the user who has the same behavior activity.

For example, send sports venue information, sports equipment information, game result information, etc. to sports enthusiasts, send seasonal foods, new foods to merchants, etc., and send the latest movie information, movie ticket discount information, etc. to the housewives. We recommend information on discounted items in shopping malls or supermarkets, information on new products, etc., recommend cigarette information and information on non-smoking areas to men who smoke, and recommend beauty products to women. Information or beauty salon activities, send the latest book information, performance information and poetry information to the literary youth, based on individual frequency statistics, according to the statistics of behavior characteristics, the same kind of people (have common behavior characteristics) People) put the same label.

Further, the smart recommendation method, after the step S200, includes S300: storing a large amount of basic information and behavior activity information of the user.

Further, in the smart recommendation method, the step S300 includes: S310: filtering a plurality of basic information and behavior activity information of the user by using a data filtering module of the network device;

S320: Store, by the data storage of the network device, basic information and behavior activity information of the user that massively filters out redundant information.

The invention also provides a recommendation system:

An intelligent recommendation system includes an intelligent mobile terminal and a network device; the smart mobile terminal includes an information collection module, a communication module, and a display module;

The information collection module is configured to acquire basic information and behavior activity information of the user;

The communication module is configured to transmit basic information and behavior activity information of the user to the network device;

The network device is configured to send recommendation information to the smart mobile terminal;

The display module is configured to graphically and/or acoustically display the recommendation information.

The basic information includes: gender, age, occupation, etc.;

The behavior activity information includes: address information, consumption information, time information of staying, and the like.

Further, in the smart recommendation system, the information collection module includes an input module, a navigation and positioning module, a time recording module, or an electronic payment module.

The input module is configured to input basic information;

The navigation and positioning module is configured to acquire latitude and longitude of the smart mobile terminal;

The time recording module is configured to determine a dwell time of the smart mobile terminal at the latitude and longitude;

The electronic payment module is configured to determine the payment situation of the smart mobile terminal at the latitude and longitude condition;

It also includes an electronic sign-in module for presenting the electronic sign-in at the time of subsequent consumption, and deducting the amount of the sign-in purchase from the pre-payment.

Further, in the smart recommendation system, the network device includes a trajectory module, a feature classification module, and a recommendation module,

The trajectory module is configured to perform a map query on a latitude and longitude of the behavior information of the user, and determine an address name of the location where the behavior activity occurs and a corresponding time;

The feature classification module is configured to encode, analyze, classify, count, and mark the basic information and behavior activity information of the user; form a user number-timestamp-longitude-latitude, and further form a data processing a set of behavioral characteristics of each of the users;

The recommendation module is configured to send recommendation information to the corresponding smart mobile terminal of the user according to the result of the categorization, statistics, and marking.

Further, in the smart recommendation system, the network device further includes a data screening module and a data storage.

The data screening module is configured to filter the basic information and behavior activity information of the uploaded user, reduce the amount of data, and remove redundant data.

The data storage is configured to store a plurality of basic information and behavior activity information of the user that filters out redundant information;

The learning module is configured to re-analyze, re-categorize, re-statistic, and re-mark the growing basic information and behavior activity information of the user to form a behavior feature set of each of the users.

The present invention has at least one of the following beneficial effects:

1. The present invention overcomes the technical problem of recommending history-related information to the user by simply accessing the user's access history or consumption history.

2. The present invention overcomes the technical problem of passively only actively obtaining information around the location of the user through the user.

3. The invention gives a powerful recommendation function of the intelligent recommendation system, including actively recommending corresponding information according to the time characteristics of the user behavior activity; and actively recommending the location around the location according to the location characteristics of the user at the time. Corresponding information related to the characteristics of the user's historical behavior activities; according to the user having the same behavioral activity characteristics, actively recommending information of the same behavioral activity characteristics to the same type of user.

4. The invention gives the intelligent recommendation system a data processing function for filtering, sorting and sorting big data.

5. The invention has the advantages of simple use, good operability, strong recommendation information and wide application.

DRAWINGS

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

1 is a schematic structural view of a system according to a first embodiment of the present invention;

2 is a schematic flow chart of a first embodiment of the present invention;

3 is a schematic diagram of a smart mobile terminal module according to a first embodiment of the present invention;

4 is a schematic diagram of a network device module according to a first embodiment of the present invention;

FIG. 5 is a schematic diagram of data flow direction according to a second embodiment of the present invention.

Description of the reference numerals

Intelligent mobile terminal-100, information collection module-110, input module 112, navigation and positioning module-113, time recording module-114, electronic payment module-115, communication module-120, display module-130; network device-200 module, Data screening module-210, data storage-220, track module-230, feature classification module-240, recommendation module-250.

detailed description

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 embodiments or the description of the prior art will be briefly described below. Obviously, the following description and the accompanying drawings are The invention is exemplified and should not be construed as limiting the invention. The following description sets forth numerous specific details to facilitate the understanding of the invention. However, in some instances, well-known or conventional details have not been described in order to satisfy the brevity of the specification.

In a typical computing hardware configuration of the present application, the client/terminal, network device, and trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The client, mobile terminal or network device in the present invention comprises a processor, including a single core processor or a multi-core processor. A processor may also be called one or more microprocessors, central processing units (CPUs), and the like. More specifically, the processor can be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, Or a processor that implements a combination of instruction sets. The processor can also be one or more dedicated processors, such as an application specific integrated circuit (ASIC), field programmable gate array (FPGA), digital signal processor (DSP), network processor, graphics processor, network processor, A communications processor, cryptographic processor, coprocessor, embedded processor, or any other type of logical component capable of processing instructions. The instructions are used by the processor to perform the operations and steps discussed herein.

The client, mobile terminal or network device in the present invention includes a memory for storing big data, and may include one or more volatile storage devices such as random access memory (RAM), dynamic RAM (DRAM), synchronous DRAM. (SDRAM), static RAM (SRAM) or other type of storage device. The memory can store information including sequences of instructions executed by the processor or any other device. For example, a variety of operating systems, device drivers, firmware (eg, input and output base systems or BIOS), and/or executable code and/or data of an application can be loaded into memory and executed by the processor.

The operating system of the client, mobile terminal or network device in the present invention may be any type of operating system, such as Microsoft's Windows, Windows Phone, Apple IOS, Google's Android, and Linux, Unix operating system or other real-time. Or an embedded operating system such as VxWorks.

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 embodiments or the description of the prior art will be briefly described below. Obviously, the following description and the accompanying drawings are The invention is exemplified and should not be construed as limiting the invention. The following description sets forth numerous specific details to facilitate the understanding of the invention. However, in some instances, well-known or conventional details have not been described in order to satisfy the brevity of the specification. Specific apparatus/systems and methods of the present invention are described in the following examples:

First embodiment

2 is a schematic flow chart of a first embodiment of the present invention:

S200: (system) acquires basic information and behavior activity information of the user;

S400: (system) screening and classifying a mass of the basic information and behavior activity information of the user;

S500: Send corresponding recommendation information to the user of the system.

Preferably, the smart recommendation method, the step S200 includes: S210: input basic information of the user by using an input module of the smart mobile terminal; the basic information includes: gender, age, occupation, and the like.

S220: Acquire, by the navigation positioning module, the time recording module, the electronic payment module, or the electronic sign-in module of the smart mobile terminal, behavior activity information of the user. The behavioral activity information includes: address information, consumption information, time information of staying, and the like.

Preferably, in the smart recommendation method, the step S400 includes:

S410: determining, by the trajectory module of the network device, a latitude and longitude of a location of occurrence of a behavior (feature) of the user in different time periods of the day;

S420: The trajectory module determines an address name of a location where the behavior activity (feature) occurs according to the latitude and longitude query map database (for example, Baidu map, Gaode map, Google map, 360 map, or self-built map database, etc.).

Address names such as: XX Hotel, XX Playground, XX Sports Hall, XX Tea House, XX Bar, XX Bookstore, XX Cinema, XX Museum, XX Health Museum, XX Beauty Salon, XX Supermarket, XX Shopping Mall, XX Tourist Attractions, etc.

Among them, the key of the S420 step is to convert the latitude and longitude information represented by the pure number into specific place information, and the relevant behavioral activity characteristics of the user can be obtained only from the specific place information.

Among them, the key of the S410 step is to attach the time information on the basis of the latitude and longitude information (address name), because the passage of a short time has no meaning for the recommendation system. The user only spends a long time at the location, indicating the user's characteristic preferences for the behavioral activity.

Preferably, in the smart recommendation method, the step S500 includes at least one of the following steps:

For example, during the annual holiday of May 1st and 11th, the system recommends travel information to users who have traveled during this time, recommend outdoor leisure information to users on weekends, and recommend hotel information to users at noon or evening.

For example, send sports venue information, sports equipment information, game result information, etc. to sports enthusiasts, send seasonal foods, new foods to merchants, etc., and send the latest movie information, movie ticket discount information, etc. to the housewives. We recommend information on discounted products in supermarkets or supermarkets, information on new products, etc., recommend cigarette information and information on non-smoking areas to men who smoke, recommend beauty products information to women or information on activities in beauty salons, and send the latest books to young artists. Information, performance information, poetry information, etc., based on individual frequency statistics, and according to the statistics of behavior characteristics, the same label (the person with common behavior characteristics) is labeled the same.

Preferably, the smart recommendation method, after the step S200, includes S300: (system) stores a large amount of basic information and behavior activity information of the user.

Preferably, the smart recommendation method, the step S300 includes: S310: filtering a plurality of basic information and behavior activity information of the user by using a data filtering module of the network device;

This embodiment further provides a recommendation system, as shown in FIG. 1 is a schematic structural diagram of a system according to a first embodiment of the present invention:

An intelligent recommendation system includes an intelligent mobile terminal 100 and a network device 200; the smart mobile terminal 100 includes an information collection module 110, a communication module 120, and a display module 130;

The information collection module 110 is configured to acquire basic information and behavior activity information of the user;

The communication module 120 is configured to transmit basic information and behavior activity information of the user to the network device;

The network device 200 is configured to send recommendation information to the smart mobile terminal;

The display module 130 is configured to display the recommendation information in a graphical and/or acoustic manner.

Among them, the display screen sends warning information in a graphical display manner; the speaker sends an early warning message in a voice manner.

FIG. 3 is a schematic diagram of a smart mobile terminal module according to a first embodiment of the present invention. Preferably, the information recommendation module 110 includes an input module 112, a navigation and positioning module 113, a time recording module 114, or an electronic device. Payment module 115,

The input module 112 is configured to input basic information; the basic information includes: gender, age, occupation, and the like; and the behavior activity information includes: address information, consumption information, time information of staying, and the like.

The navigation and positioning module 113 is configured to acquire the latitude and longitude of the user's intelligent mobile terminal; the navigation and positioning module includes a GPS system sub-module, a Galileo system sub-module, a GLONASS system sub-module, or a Beidou system sub-module.

The time recording module 114 is configured to determine a dwell time of the smart mobile terminal at the latitude and longitude;

The electronic payment module 115 is configured to determine a payment situation of the smart mobile terminal in the latitude and longitude;

FIG. 4 is a schematic diagram of a network device module according to a first embodiment of the present invention. Preferably, the smart recommendation system includes a trajectory module 230, a feature classification module 240, and a recommendation module 250.

The trajectory module 230 is configured to perform latitude and longitude on the behavior information of the user a map query to determine an address name of the place where the behavior activity occurs and a corresponding time;

The feature classification module 240 is configured to encode, analyze, classify, count, and mark a plurality of basic information and behavior activity information of the user; form a user number-timestamp-longitude-latitude, and further form each of the Describe the user's behavioral feature set; filtering, analyzing, mining, and classifying big data to provide a technical basis for subsequent extraction of classification features and recommendation of corresponding information to corresponding people;

The recommendation module 250 is configured to send recommendation information to the corresponding smart mobile terminal of the user according to the result of the classification, statistics, and marking.

Preferably, the smart recommendation system further includes a data filtering module 210 and a data storage 220.

The data screening module 210 is configured to filter the uploaded basic information and behavior activity information of the user; reduce the amount of data, and remove redundant data.

The data storage 220 is configured to store a large amount of basic information and behavior activity information of the user that filters out redundant information;

The network device further includes a learning module for reanalysing, reclassifying, re-stating, and re-marking the growing basic information and behavioral activity information of the user to form a behavior feature set of each of the users.

Second embodiment

The system provided in this embodiment is composed of four large blocks.

First, the collection of massive user data;

2. Receiving and storing data of a large number of users (based on location data and/or time nodes);

3. Analysis and processing of data by massive users (based on location data and/or time nodes);

4. Push the corresponding recommendation information to the same type of users through the recommendation algorithm.

The user's historical trajectory is composed of time-continuous location points. In order to record these data, the system's server needs to receive and process data at a very high frequency (for example, once every 10 seconds), the amount of users accumulated per day * the amount of user data, Even T-levels can be reached, so massive amounts of big data are stored in data store 220, a distributed system infrastructure consisting of Hadoop. The system's solution to big data The main solution is to proceed from the following two aspects:

FIG. 5 is a schematic diagram of data flow direction according to a second embodiment of the present invention. On the one hand, starting from the system, the performance of the system is improved. The data screening module 210 uses the spark streaming distributed real-time data processing engine to receive and process data, load balance the server, and make the system real-time.

On the other hand, starting from the data itself, data filtering is performed to reduce the amount of data. Location data itself is characterized by redundancy and repeatability. For example, if a user can rest for more than a few hours at night, the data in this time period is highly repetitive, and there is a need for screening and culling in terms of data storage, communication, and processing. That is to say, the location data is clustered, and the representative location data is selected, which may be referred to as the user's trajectory feature point.

The trajectory module 230 calculates that the difference between the geographic location data represents the distance (the calculation method is the squared difference formula), and according to this feature, the K-Means clustering algorithm is adopted, and the clustering is based on the square difference being smaller than the K value (K value according to the data) The scale of the quantity is dynamically adjusted under the two factors of whether the trajectory feature points remain representative and server performance after balanced clustering. According to the above, a trajectory feature point set (referred to as a feature data set) of the user capable of embodying the user behavior feature is obtained.

For the training of the user feature data set, the machine learning algorithm applied by the feature classification module 240 is an item-based collaborative filtering algorithm (collaborative filtering is generally divided into user-based and item-based). The reason is that the item items extracted by the data set are relatively stable in scale, and the number of users is constantly increasing. Using this algorithm in accordance with the characteristics of the recommended system can reduce the overhead of the system.

The format of the feature dataset is user number-timestamp-longitude-latitude (the location is to be combined with the address name). The POI of the corresponding location is obtained from the location data, and the location feature is extracted according to the POI, and is used to analyze the user's life trajectory (time + location) and habit hobbies (behavior frequency, time + behavior, location + behavior). This module gets a set of behavioral characteristics for each user.

Recommendation Module 250, the key to making recommendations is to recommend the right project at the right time. The process is to first analyze the set of behavioral characteristics, starting from three points,

The first point requests the POI information of the location according to the current location information of the user, matches the behavior feature set of the user, and filters out the first few recommendations with high matching degree.

The second point analyzes the behavior of the current time frequency in the user history according to the behavior feature set, and recommends to the user. The above two points are the analysis results starting from the user's own preferences.

The third point is to discover the potential hobbies of users by comparing the behavior characteristics of different users, and the users with higher similarity match each other. For example, user A and user B have similar behavioral feature sets, that is, they have common interests, so the system classifies them as similar people. When recommending user A, they can search for the same behavioral feature set. A project different from A's behavioral feature set is recommended to User A. That is to recommend to A a project that he may like but still do not know, that is, potential hobbies. The theoretical premise is that similar users are more likely to agree on an unknown behavior.

A new direction based on geographic location data and/or time node data service is proposed. Not only the geographical location data and time node data are used from the surface, but the analysis is deeper and deeper, and the value of geographical location data and time node data is improved. The traditional recommendation algorithm adds location data to make the recommendation smarter and more accurate.

It is apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the invention is defined by the appended claims instead All changes in the meaning and scope of equivalent elements are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim. In addition, it is to be understood that the word "comprising" does not exclude other elements or steps. A plurality of units or devices recited in the device claims may also be implemented by a unit or device by software or hardware. The first, second, etc. words are used to denote names and do not denote any particular order.

Claims

An intelligent recommendation method, characterized in that the method comprises the following steps:

S200: Obtain basic information and behavior activity information of the user;

S400: screening and classifying a mass of the basic information and behavior activity information of the user;

S500: Send corresponding recommendation information to the user.
The smart recommendation method according to claim 1, wherein the step S200 comprises: S210: inputting basic information of the user through an input module of the smart mobile terminal;

S220: Acquire, by the navigation positioning module, the time recording module, and the electronic payment module of the smart mobile terminal, behavior activity information of the user.
The smart recommendation method according to claim 1, wherein the step S400 comprises: S410: determining, by the trajectory module of the network device, a latitude and longitude of a location of occurrence of a behavior activity of the user in different time periods of the day;

S420: The trajectory module determines an address name of a location where the behavior activity occurs according to the latitude and longitude query map database.
The smart recommendation method according to claim 1, wherein the step S500 comprises at least one of the following steps:

S510: The recommendation module of the network device sends the corresponding recommendation information to the user, where the recommendation information is the behavior activity of the user that occurs in the same time period in the history record. Frequency-ordered behavioral activity related venue information;

S520: When the user is in a certain location, the recommendation module of the network device sends the corresponding recommendation information to the user, where the recommendation information is sorted by the frequency of the behavior activity of the user around the certain location. The behavior related activity information;

S530: The recommendation module of the network device sends the same recommendation information to the user who has the same behavior activity.
The intelligent recommendation method according to claim 1, wherein the step S200
An intelligent recommendation method, characterized in that the method comprises the following steps:

S200: Obtain basic information and behavior activity information of the user;

S400: screening and classifying a mass of the basic information and behavior activity information of the user;

S500: Send corresponding recommendation information to the user.
The smart recommendation method according to claim 1, wherein the step S200 comprises: S210: inputting basic information of the user through an input module of the smart mobile terminal;

S220: Acquire, by the navigation positioning module, the time recording module, and the electronic payment module of the smart mobile terminal, behavior activity information of the user.
The smart recommendation method according to claim 1, wherein the step S400 comprises: S410: determining, by the trajectory module of the network device, a latitude and longitude of a location of occurrence of a behavior activity of the user in different time periods of the day;

S420: The trajectory module determines an address name of a location where the behavior activity occurs according to the latitude and longitude query map database.
The smart recommendation method according to claim 1, wherein the step S500 comprises at least one of the following steps:

S510: The recommendation module of the network device sends the corresponding recommendation information to the user, where the recommendation information is the behavior activity of the user that occurs in the same time period in the history record. Frequency-ordered behavioral activity related venue information;

S520: When the user is in a certain location, the recommendation module of the network device sends the corresponding recommendation information to the user, where the recommendation information is sorted by the frequency of the behavior activity of the user around the certain location. The behavior related activity information;

S530: The recommendation module of the network device sends the same recommendation information to the user who has the same behavior activity.
The intelligent recommendation method according to claim 1, wherein the step S200 The latter includes S300: storing a large amount of basic information and behavioral activity information of the user.
The smart recommendation method according to claim 5, wherein the step S300 comprises: S310: filtering basic information and behavior activity information of the user by using a data filtering module of the network device;

S320: Store, by the data storage of the network device, basic information and behavior activity information of the user that filters out redundant information.
An intelligent recommendation system, comprising: an intelligent mobile terminal and a network device; the smart mobile terminal comprises an information collection module, a communication module and a display module;

The information collection module is configured to acquire basic information and behavior activity information of the user;

The communication module is configured to transmit basic information and behavior activity information of the user to the network device;

The network device is configured to send recommendation information to the smart mobile terminal;

The display module is configured to graphically and/or acoustically display the recommendation information.
The intelligent recommendation system according to claim 7, wherein the information collection module comprises an input module, a navigation and positioning module, a time recording module or an electronic payment module.

The input module is configured to input basic information;

The navigation and positioning module is configured to acquire latitude and longitude of the smart mobile terminal;

The time recording module is configured to determine a dwell time of the smart mobile terminal at the latitude and longitude;

The electronic payment module is configured to determine a payment situation of the smart mobile terminal at the latitude and longitude.
The smart recommendation system according to claim 7, wherein the network device comprises a trajectory module, a feature classification module and a recommendation module,

The trajectory module is configured to perform a map query on a latitude and longitude of the behavior information of the user, and determine an address name of the location where the behavior activity occurs and a corresponding time;

The feature classification module is configured to import a large amount of basic information and behavior activity information of the user The latter includes S300: storing a large amount of basic information and behavioral activity information of the user.
The smart recommendation method according to claim 5, wherein the step S300 comprises: S310: filtering basic information and behavior activity information of the user by using a data filtering module of the network device;

S320: Store, by the data storage of the network device, basic information and behavior activity information of the user that filters out redundant information.
An intelligent recommendation system, comprising: an intelligent mobile terminal and a network device; the smart mobile terminal comprises an information collection module, a communication module and a display module;

The information collection module is configured to acquire basic information and behavior activity information of the user;

The communication module is configured to transmit basic information and behavior activity information of the user to the network device;

The network device is configured to send recommendation information to the smart mobile terminal;

The display module is configured to graphically and/or acoustically display the recommendation information.
The intelligent recommendation system according to claim 7, wherein the information collection module comprises an input module, a navigation and positioning module, a time recording module or an electronic payment module.

The input module is configured to input basic information;

The navigation and positioning module is configured to acquire latitude and longitude of the smart mobile terminal;

The time recording module is configured to determine a dwell time of the smart mobile terminal at the latitude and longitude;

The electronic payment module is configured to determine a payment situation of the smart mobile terminal at the latitude and longitude.
The smart recommendation system according to claim 7, wherein the network device comprises a trajectory module, a feature classification module and a recommendation module,

The trajectory module is configured to perform a map query on a latitude and longitude of the behavior information of the user, and determine an address name of the location where the behavior activity occurs and a corresponding time;

The feature classification module is configured to import a large amount of basic information and behavior activity information of the user Row coding, analysis, categorization, statistics, and tagging to form a set of behavioral characteristics for each of the users;

The recommendation module is configured to send recommendation information to the corresponding smart mobile terminal of the user according to the result of the categorization, statistics, and marking.
The intelligent recommendation system according to claim 9, wherein the network device further comprises a data filtering module and a data storage.

The data filtering module is configured to filter the uploaded basic information and behavior activity information of the user;

The data storage is configured to store a large amount of basic information and behavior activity information of the user that filters out redundant information.