CN103617547B - A kind of business recommended method and system - Google Patents
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Abstract
The raw information of internet, and user is used to use the position context information of mobile service the invention discloses a kind of business recommended method and system, including acquisition user basic information, user；User according to obtaining sets up user interest model using the raw information of internet；User according to obtaining sets up user situation model using the position context information of mobile service；The association relation model set up between user interest model, user situation model and business information, according to association relation model, calculates the recommendation degree of each business recommended to user, and by degree of recommendation service propelling high to user.In the technical scheme that the present invention is provided, customer location scene is incorporated into individual business recommendation method, improves personalized service recommendation precision and Consumer's Experience；And system framework of the present invention based on cloud computing resource pool, elasticity, the dynamic expansion of resource are realized, system response time is shortened, while improving Consumer's Experience.
Description
Technical Field
The invention relates to a cloud computing technology, in particular to a cloud service personalized business recommendation method and system based on Location Based Service (LBS).
Background
At present, the public service and the fine operation become an important means for operators to develop services and improve user experience. The existing accurate marketing system mainly finds a business operation rule based on data such as basic information, geographical position information and communication behaviors of a user, and then carries out business recommendation.
In the existing LBS-based service recommendation service system, only geographical position information and only communication behavior information of service categories are considered, and user interest preference and position situation preference are not considered, so that services recommended to users are not the most desirable for the users, and the problem of poor user experience is caused.
Disclosure of Invention
In order to solve the technical problem, the invention provides a service recommendation method and a service recommendation system, which can respond to user requirements in real time and improve user experience.
In order to achieve the purpose of the invention, the invention provides a service recommendation method, which comprises the following steps: acquiring basic information of a user, original information of the user using the Internet and position scene information of the user using mobile services;
establishing a user interest model according to the obtained original information of the user using the Internet; establishing a user scene model according to the obtained position scene information of the mobile service used by the user;
and establishing an incidence relation model among the user interest model, the user scene model and the service information, calculating the recommendation degree of recommending new services to the user according to the incidence relation model, and pushing the services with high recommendation degree to the user.
The user basic information at least comprises: user identity information and ordering service information;
the original information of the user using the internet is URL log information.
The establishing of the user interest model comprises the following steps:
obtaining the subject category of the webpage document accessed by the user through analyzing the URL log information;
establishing corresponding relations between different interest categories and interest degrees;
the interest degree is in direct proportion to the number of the class web page documents contained in the corresponding interest category, and in inverse proportion to the time difference of the web page documents contained in the recently viewed interest category.
The method further comprises the following steps: and updating the user interest model according to a preset timing duration.
The location context information of the user using the mobile service includes: location information, time information, terminal device information, and service information.
The establishing of the user context model comprises the following steps:
and establishing corresponding relations among positions, time, terminals, services and the interest degrees of the position scenes according to different position scene types.
The establishing of the incidence relation model among the user interest model, the user scenario model and the service information comprises the following steps:
and calculating the vector product of the user interest model of each user and the user context modularity to obtain an incidence relation model.
The step of pushing the service with high recommendation degree to the user comprises the following steps:
calculating the recommendation degree of recommending new services to the user according to the incidence relation model;
and sequencing the recommendation degrees of the new services, and pushing the services with the recommendation degrees of the services arranged in the previous service recommendation quantity threshold item to the user according to a service recommendation quantity threshold preset by the system.
The invention also recommends a service recommendation system, at least include obtaining the unit, first building unit, second building unit, third building unit, and the service recommendation unit; wherein,
the acquisition module is used for acquiring the basic information of the user, the original information of the user using the Internet and the position scene information of the user using the mobile service;
the first establishing unit is used for establishing a user interest model according to the obtained original information of the user using the Internet;
the second establishing unit is used for establishing a user context model according to the obtained position context information of the mobile service used by the user;
the third establishing unit is used for establishing an incidence relation model between the user interest model and the user context model and the service information according to the established user interest model and the user context model;
and the service recommendation unit is used for calculating the recommendation degree of each service recommended to the user according to the association relation model and pushing the service with high recommendation degree to the user according to the basic information of the user.
The service recommendation system is located in the cloud computing resource pool.
Compared with the prior art, the method comprises the steps of obtaining basic information of a user, original information of the user using the internet and position scene information of the user using the mobile service; establishing a user interest model according to the obtained original information of the user using the Internet; establishing a user scene model according to the obtained position scene information of the mobile service used by the user; and establishing an incidence relation model among the user interest model, the user scene model and the service information, calculating the recommendation degree of each service recommended to the user according to the incidence relation model, and pushing the service with high recommendation degree to the user. According to the technical scheme provided by the invention, the user position situation established according to the user interest is introduced into the personalized service recommendation method, so that the personalized service recommendation accuracy and the user experience are improved; in addition, the system framework based on the cloud computing resource pool realizes the elastic and dynamic expansion of resources, shortens the response time of the system and simultaneously improves the user experience.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flow chart of a service recommendation method of the present invention;
fig. 2 is a schematic structural diagram of a service recommendation system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
Fig. 1 is a flowchart of a service recommendation method of the present invention, as shown in fig. 1, including the following steps:
step 100: and acquiring basic information of the user, original information of the user using the Internet and position scene information of the user using the mobile service.
In this step, the basic user information, such as user identity information, subscription service information, etc., may be collected from a service Support System (BSS)/Operation Support System (OSS);
collecting original information (accessed Uniform Resource Locator (URL) log information) of users using the Internet from a Broadband Access Server (BRAS) and a Remote Authentication Dial In User Service (RADIUS), such as (A)URL1；URL2；Λ）；
And acquiring the position scene information of the mobile service used by the user by a mobile base station positioning device arranged on the mobile base station. The location context information of the user using the mobile service includes the following 4 types of information: (1) location information, including location name LNameAnd its precise longitude information LLongitudeAnd latitude information LLatitude(ii) a (2) Time information, including date LDateAnd a specific time LTime(ii) a (3) Terminal equipment information, i.e. type L of mobile terminal equipmentTerminal(ii) a (4) Service information LServiceThat is, the location context information of the user using the mobile service includes the following sets: (L)Name,LLongitude,LLatitude,LDate,LTime,LTerminal，LService)。
The specific implementation of this step is a routine technical means for those skilled in the art, and the specific implementation is not used to limit the protection scope of the present invention, and is not described herein again.
Step 101: establishing a user interest model according to the obtained original information of the user using the Internet; and establishing a user context model according to the obtained position context information of the mobile service used by the user.
In this step, the specific implementation of establishing the user interest model according to the obtained original information of the user using the internet includes:
first, by analyzing the obtained original information (accessed URL log information) of the user using the Internet, that is, by using the existing text mining technology, the subject category such as sports category of the page corresponding to each URL is obtained, and the user interest model is establishedAs shown in the formula (1),
in the formula (1), m represents the interest quantity of the current user, and m is more than 0 and less than or equal to | C |; | C | is the total number of system topic categories, (C)i,wi) Is the (i +1) th (i is more than or equal to 0 and less than m) type interest item of the user, ciIndicates the name of the interest category, wiDenotes ciInterestingness of class interests. Namely, the corresponding relation between different interest categories and the interest degrees is established.
Because the interest of the user can dynamically change along with the time, some content of the original interest of the user can be forgotten along with the time, and new interest can be generated gradually, therefore, in the formula (1), the interest degree w of the (i +1) (0 ≦ i < m) class interest of the useriC of interest to the useriThe number of the similar web page documents is in direct proportion to the number of the recently read ciThe time difference of the class web documents is inversely proportional. Thus, the interestingness w of the (i +1) (0 ≦ i < m) th class of interests of the useriAs shown in the formula (2),
in the formula (2), niIndicating the URL log information of the current useriNumber of class URL logs, tiIndicating the URL log information of the current userithe latest occurrence time of the URL-like log, T represents the current system time, α > 0 is an adjustment coefficient for preventing the denominator of the f () function in formula (2) from being 0, and at the same time, the interestingness w can be adjustedithe smaller the value of alpha, the smaller the interest withe faster the decay rate of (a), therefore, the value of α can be determined according to specific requirements.
The method also comprises the following steps: and periodically updating the user interest model according to a preset timing duration.
In this step, a user context model is established according to the obtained location context information of the mobile service used by the userAccording to the location information (including the location name L)NameAnd its precise longitude information LLongitudeAnd latitude information LLatitude) Classify the location, such as dining, entertainment, hotels, etc., to obtain Lj(ii) a Based on time information (including date L)DateAnd a specific time LTime) Classifying the time, such as working day/holiday, morning/afternoon/evening, etc., to obtain Tj；TerminaljThat is the type L of the terminal deviceTerminal(ii) a According to the service information LServiceClassifying the Service to obtain Servicej. As shown in equation (3):
in formula (3), p represents the number of location context types of the current user, (L)j,Tj,Terminalj，Servicej，ξj) The (j +1) th (j is more than or equal to 0 and less than or equal to p-1) class position scene item, L, of the current userjIndicating the location class name, TjIndicates the time class name, TerminaljIndicating the terminal class name, Servicejindicating the name of the service class, ξjIs the interestingness of the (j +1) th class location scenario.
It should be noted that, for each user U in the system, where U is greater than or equal to 1 and less than or equal to | U |, and | U | is the total number of users in the system, a user interest model is established according to the obtained original information of the user using the internet, and a user context model is established according to the obtained location context information of the user using the mobile service.
Step 102: and establishing an incidence relation model among the user interest model, the user scene model and the service information.
According to user interest model of each user in the systemUser context modelModel of association relation between business informationAs shown in equation (4):
in the formula (4), the first and second groups,representing the individual users in the system and,u is more than or equal to 1 and less than or equal to | U |, and | U | is the total number of users.
Step 103: and calculating the recommendation degree of recommending new services to the user according to the incidence relation model, and pushing the services with high recommendation degree to the user.
In this step, the correlation model is first usedCalculation of recommendation of new Service to user A (assuming user A has 1-a interests)kDegree of recommendation RkAs shown in equation (5):
in formula (5), l represents a interests of the user a.
Then, the new Service is usedkDegree of recommendation RkPerforming descending order arrangement, and according to a service recommendation quantity threshold Num preset by the system, determining the recommendation degree RkNew Service arranged in previous Num itemkPushed to user a.
In the method, the user position situation established according to the user interest is introduced into the LBS personalized service recommendation method, so that the personalized service recommendation accuracy and the user experience are improved; moreover, the method is applied to a system framework based on the cloud computing resource pool, so that the elastic and dynamic expansion of resources is realized, the system response time is shortened, and the user experience is improved.
Fig. 2 is a schematic diagram of a configuration structure of the service recommendation system of the present invention, as shown in fig. 2, which at least includes an obtaining unit, a first establishing unit, a second establishing unit, a third establishing unit, and a service recommendation unit; wherein,
the acquisition module is used for acquiring the basic information of the user, the original information of the user using the Internet and the position scene information of the user using the mobile service;
the first establishing unit is used for establishing a user interest model according to the obtained original information of the user using the Internet;
the second establishing unit is used for establishing a user context model according to the obtained position context information of the mobile service used by the user;
and the third establishing unit is used for establishing an incidence relation model among the user interest model, the user scene model and the service information according to the established user interest model and the user scene model.
And the service recommendation unit is used for calculating the recommendation degree of each service recommended to the user according to the association relation model and pushing the service with high recommendation degree to the user according to the basic information of the user.
The service recommendation system is positioned in the cloud computing resource pool, and the cloud computing resource pool comprises computing and storage resources distributed in different geographic positions, so that the required computing and storage resources are dynamically and transparently provided according to different requirements of relevant application programs of the personalized service recommendation system through a scheduling strategy of the cloud computing resource management component under a cloud computing framework, and the resources of the application programs are dynamically recycled when the current application programs are not used. In other words, in the cloud computing resource pool-based system framework, the elastic and dynamic expansion of resources is realized, the system response time is shortened, and the user experience is improved.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The service recommendation method is applied to a system framework based on a cloud computing resource pool, and comprises the following steps:
acquiring basic information of a user, original information of the user using the Internet and position scene information of the user using mobile services; the location context information of the user using the mobile service includes: position information, time information, terminal equipment information and service information; the user uses the original information of the internet as URL log information;
establishing a user interest model according to the obtained original information of the user using the Internet; the method for establishing the user context model according to the position context information of the mobile service used by the user, which is obtained from a mobile base station positioning device arranged on a mobile base station, comprises the following steps: establishing corresponding relations among positions, time, terminals, services and interest degrees of the position scenes according to different position scene types;
establishing an incidence relation model among a user interest model, a user scene model and service information, calculating a recommendation degree of recommending new services to a user according to the incidence relation model, and pushing services with high recommendation degrees to the user;
the establishing of the incidence relation model among the user interest model, the user scenario model and the service information comprises the following steps: and calculating the vector product of the user interest model of each user and the user context modularity to obtain an incidence relation model.
2. The service recommendation method according to claim 1, wherein said user basic information at least comprises: user identity information and subscription service information.
3. The service recommendation method according to claim 2, wherein said establishing a user interest model comprises:
obtaining the subject category of the webpage document accessed by the user through analyzing the URL log information;
establishing corresponding relations between different interest categories and interest degrees;
the interest degree is in direct proportion to the number of the class web page documents contained in the corresponding interest category, and in inverse proportion to the time difference of the web page documents contained in the recently viewed interest category.
4. A service recommendation method according to claim 2 or 3, characterized in that the method further comprises: and updating the user interest model according to a preset timing duration.
5. The service recommendation method according to claim 1, wherein said pushing the service with high recommendation degree to the user comprises:
calculating the recommendation degree of recommending new services to the user according to the incidence relation model;
and sequencing the recommendation degrees of the new services, and pushing the services with the recommendation degrees of the services arranged in the previous service recommendation quantity threshold item to the user according to a service recommendation quantity threshold preset by the system.
6. A service recommendation system is characterized by being located in a cloud computing resource pool and at least comprising an acquisition unit, a first establishment unit, a second establishment unit, a third establishment unit and a service recommendation unit; wherein,
the acquisition module is used for acquiring the basic information of the user, the original information of the user using the Internet and the position scene information of the user using the mobile service; the location context information of the user using the mobile service includes: position information, time information, terminal equipment information and service information;
the first establishing unit is used for establishing a user interest model according to the obtained original information of the user using the Internet; the user uses the original information of the internet as URL log information;
a second establishing unit configured to establish a user context model based on location context information of a user using a mobile service, which is obtained from a mobile base station positioning device in which a mobile base station is set, the second establishing unit including: establishing corresponding relations among positions, time, terminals, services and interest degrees of the position scenes according to different position scene types;
the third establishing unit is used for establishing an incidence relation model between the user interest model and the user context model and the service information according to the established user interest model and the user context model; specifically, the method is used for calculating the vector product of the user interest model of each user and the user context modularity to obtain an incidence relation model;
and the service recommendation unit is used for calculating the recommendation degree of each service recommended to the user according to the association relation model and pushing the service with high recommendation degree to the user according to the basic information of the user.
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