CN103617547A - Service recommendation method and system - Google Patents
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Abstract
The invention discloses a service recommendation method and system. The method comprises the steps of obtaining essential information of users, raw information of using the internet by the users and position scene information of using mobile service by the users; setting a user interest model according to the obtained raw information of using the internet by the users; setting a user scene model according to the obtained position scene information of using the mobile service by the users; and setting an association relationship model among the user interest model, the user scene model and service information, calculating the recommendation levels of various kinds of service recommended to the users according to the association relationship model, and recommending the service with the high recommendation level to the users. According to the technical scheme, the position scene of the users is introduced into the individual service recommendation method, so that the recommendation accuracy of individual service and the user experience are improved, and the method is based on a system framework of a cloud computing resource pool, so that the flexibility and the dynamic propagation of resources are achieved, the system response time is shortened and the user experience is improved.
Description
Technical field
The present invention relates to cloud computing technology, the business recommended method and system of the cloud service personalization of espespecially a kind of position-based service (LBS).
Background technology
At present, Focus service, fine management have become operator's development service, have promoted a kind of important means that user experiences.Existing precision marketing system, mainly based on to data such as user's essential information, geographical location information, communication behaviors, is therefrom found service operation rule, and then the recommendation of commencing business.
In the existing business recommended service system based on LBS, due to the communication behavior information of only considering geographical location information and only having class of service, do not consider user interest preference and position sight preference, like this, the business of recommending to user is not that user wants most, has caused user to experience poor problem.
Summary of the invention
In order to solve the problems of the technologies described above, the invention provides a kind of business recommended method and system, can real-time response user's request, promote user and experience.
In order to reach the object of the invention, the invention provides a kind of business recommended method, comprising: obtain the raw information that user basic information, user are used internet, and user uses the position context information of mobile service;
According to the user who obtains, use the raw information of internet to set up user interest model; According to the user who obtains, use the position context information of mobile service to set up user situation model;
Set up the association relation model between user interest model, user situation model and business information, according to association relation model, calculate and to recommend the recommendation degree of new business to user, and by the high service propelling of recommendation degree to user.
Described user basic information at least comprises: subscriber identity information, order business information;
It is URL log information that described user uses the raw information of internet.
The described user interest model of setting up comprises:
By the analysis to described URL log information, obtain the subject categories of user's accessed web page document;
Set up the corresponding relation of different category of interest and interest-degree;
Wherein, the class web document number that described interest-degree comprises with corresponding category of interest is directly proportional, and the mistiming of the web document comprising with the category of interest of nearest reading is inversely proportional to.
The method also comprises: according to the timing length setting in advance, upgrade described user interest model.
Described user uses the position context information of mobile service to comprise: positional information, temporal information, terminal device information and business information.
The described user situation model of setting up comprises:
According to different position scenario type, set up position, time, terminal, business, and the corresponding relation between the interest-degree of position sight.
The described association relation model of setting up between user interest model, user situation model and business information comprises:
Calculate described each user's user interest model and the vector product of user situation modularity, obtained association relation model.
Described the high service propelling of recommendation degree is comprised to user:
According to described association relation model, calculate and recommend the recommendation degree of new business to user;
The recommendation degree of new business is sorted, and according to the business recommended amount threshold of systemic presupposition, the service propelling that the recommendation degree of business is arranged in front to business recommended amount threshold item is to user.
The present invention also recommends a kind of business recommended system, at least comprises that acquiring unit, first sets up unit, second and set up unit, the 3rd and set up unit, and business recommended unit; Wherein,
Acquisition module, use the raw information of internet, and user uses the position context information of mobile service for user basic information, user;
First sets up unit, for using the raw information of internet to set up user interest model according to the user who obtains;
Second sets up unit, for using the position context information of mobile service to set up user situation model according to the user who obtains;
The 3rd sets up unit, for according to user interest model and the user situation model set up, sets up the association relation model between user interest model, user situation model and business information;
Business recommended unit, for according to association relation model, calculates the recommendation degree of each business of recommending to user, and according to user basic information by the high service propelling of recommendation degree to user.
Described business recommended system is arranged in cloud computing resource pool.
Compared with prior art, the present invention includes and obtain the raw information that user basic information, user are used internet, and user uses the position context information of mobile service; According to the user who obtains, use the raw information of internet to set up user interest model; According to the user who obtains, use the position context information of mobile service to set up user situation model; Set up the association relation model between user interest model, user situation model and business information, according to association relation model, calculate the recommendation degree of each business of recommending to user, and by the high service propelling of recommendation degree to user.In technical scheme provided by the invention, the customer location sight of setting up according to user interest is incorporated in individual business recommend method, has promoted personalized service recommendation precision and user and experienced; And the present invention is based on the system framework of cloud computing resource pool, and realized elasticity, the dynamic expansion of resource, shortened system response time, also promoted user's experience simultaneously.
Other features and advantages of the present invention will be set forth in the following description, and, partly from instructions, become apparent, or understand by implementing the present invention.Object of the present invention and other advantages can be realized and be obtained by specifically noted structure in instructions, claims and accompanying drawing.
Accompanying drawing explanation
Accompanying drawing is used to provide the further understanding to technical solution of the present invention, and forms a part for instructions, is used from explanation technical scheme of the present invention with the application's embodiment mono-, does not form the restriction to technical solution of the present invention.
Fig. 1 is the process flow diagram of the business recommended method of the present invention;
Fig. 2 is the composition structural representation of the business recommended system of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearer, hereinafter in connection with accompanying drawing, embodiments of the invention are elaborated.It should be noted that, in the situation that not conflicting, the embodiment in the application and the feature in embodiment be combination in any mutually.
In the step shown in the process flow diagram of accompanying drawing, can in the computer system such as one group of computer executable instructions, carry out.And, although there is shown logical order in flow process, in some cases, can carry out shown or described step with the order being different from herein.
Fig. 1 is the process flow diagram of the business recommended method of the present invention, as shown in Figure 1, comprises the following steps:
Step 100: obtain the raw information that user basic information, user are used internet, and user uses the position context information of mobile service.
In this step, can gather user basic information as subscriber identity information, order business information etc. from business support system (BSS, Business Support System)/operation support system (OSS, Operation Support System);
From BAS Broadband Access Server (BRAS, Broadband Remote Access Server) and remote customer dialing authentication system (RADIUS, Remote Authentication Dial In User Service) gather the raw information (URL(uniform resource locator) of access (URL) log information) that user uses internet, as (
uRL
1;
uRL
2; Λ);
By being arranged on the mobile base station locating device of mobile base station, obtain the position context information that user uses mobile service.User uses the position context information of mobile service to comprise following 4 category informations: (1) positional information, comprises location name L
name, and accurate longitude information L
longitudewith latitude information L
latitude; (2) temporal information, comprises date L
datewith concrete time L
time; (3) terminal device information, i.e. the type L of mobile terminal device
terminal; (4) business information L
service, that is to say, user uses the position context information of mobile service to comprise following set: (L
name, L
longitude, L
latitude, L
date, L
time, L
terminal, L
service).
The specific implementation of this step belongs to those skilled in the art's conventional techniques means, and the protection domain that specific implementation is not intended to limit the present invention, repeats no more here.
Step 101: use the raw information of internet to set up user interest model according to the user who obtains; According to the user who obtains, use the position context information of mobile service to set up user situation model.
In this step, the specific implementation of using the raw information of internet to set up user interest model according to the user who obtains comprises:
First, by the user who obtains being used to the analysis of the raw information (the URL log information of access) of internet, use existing Text Mining Technology, obtain the subject categories of the corresponding page of each URL as sport category, set up user interest model
as shown in formula (1),
In formula (1), m represents active user's interest quantity, 0 < m≤| C|; | C| is system subject categories sum, (c
i, w
i) be (i+1) (0≤i < m) class item of interest of this user, c
irepresent category of interest title, w
irepresent c
ithe interest-degree of class interest.Set up the corresponding relation of different category of interest and interest-degree.
Due to user's hobby can be in time passing and dynamic change, some users passing that interested content can be in time originally and gradually forgeing, new interest can produce gradually, therefore, in formula (1), the interest-degree w of user's (i+1) (0≤i < m) class interest
iwith the interested c of user
iclass web document number is directly proportional, with nearest reading c
ithe mistiming of class web document is inversely proportional to.Therefore, the interest-degree w of user (i+1) (0≤i < m) class interest
ias shown in formula (2),
In formula (2), n
irepresent c in active user URL log information
ithe quantity of class URL daily record, t
irepresent c in active user URL log information
ithe nearest time of occurrence of class URL daily record, T represents the current system time, α > 0 is one and adjusts coefficient, for preventing that the denominator of the f () function of formula (2) from being 0, meanwhile, can also adjust interest-degree w
ithe rate of decay, α value is less, interest-degree w
ithe rate of decay faster, therefore, can determine according to real needs the value of α.
This step also comprises: regularly according to the timing length setting in advance, periodically carry out the renewal of user interest model.
In this step, according to the user who obtains, use the position context information of mobile service to set up user situation model
according to positional information, (comprise location name L
name, and accurate longitude information L
longitudewith latitude information L
latitude) classified in position, as food and drink, amusement, hotel etc., obtain L
j; According to temporal information, (comprise date L
datewith concrete time L
time) time is classified, as working day/festivals or holidays, am/pm/evening etc., obtain T
j; Terminal
jbe exactly the type L of terminal device
terminal; According to business information L
servicebusiness is classified, obtain Service
j.As shown in formula (3):
In formula (3), p represents the quantity of active user's position scenario type, (L
j, T
j, Terminal
j, Service
j, ξ
j) represent (j+1) (0≤j≤p-1) class position sight item of current this user, L
jrepresent position classification title, T
jexpression time item name, Terminal
jrepresent terminal class title, Service
jrepresent class of service title, ξ
jit is the interest-degree of (j+1) class position sight.
It should be noted that, for each user u in system, wherein, 1≤u≤| U|, | U| is the total number of users in system, capital is used the raw information of internet to set up user interest model according to the user who obtains respectively, according to the user who obtains, uses the position context information of mobile service to set up user situation model.
Step 102: set up the association relation model between user interest model, user situation model and business information.
According to the user interest model of each user in system
user situation model
and the association relation model between business information
as shown in formula (4):
In formula (4),
each user in expression system,
1≤u≤| U|, | U| is total number of users.
Step 103: according to association relation model, calculate and to recommend the recommendation degree of new business to user, and by the high service propelling of recommendation degree to user.
In this step, first according to association relation model
calculating has 1～a interest to user A(hypothesis user A) recommendation new business Service
krecommendation degree R
kas shown in formula (5):
In formula (5), l represents a the interest of user A.
Then, by new business Service
krecommendation degree R
kcarry out descending sort, according to the business recommended amount threshold Num of systemic presupposition, will degree of recommendation R
kbe arranged in front the new business Service of Num item
kbe pushed to user A.
In the inventive method, the customer location sight of setting up according to user interest is incorporated in LBS individual business recommend method, has promoted personalized service recommendation precision and user and experienced; And the inventive method is applied in the system framework based on cloud computing resource pool, realized elasticity, the dynamic expansion of resource, shortened system response time, also promoted user's experience simultaneously.
Fig. 2 is the composition structural representation of the business recommended system of the present invention, as shown in Figure 2, at least comprises that acquiring unit, first sets up unit, second and set up unit, the 3rd and set up unit, and business recommended unit; Wherein,
Acquisition module, use the raw information of internet, and user uses the position context information of mobile service for user basic information, user;
First sets up unit, for using the raw information of internet to set up user interest model according to the user who obtains;
Second sets up unit, for using the position context information of mobile service to set up user situation model according to the user who obtains;
The 3rd sets up unit, for according to user interest model and the user situation model set up, sets up the association relation model between user interest model, user situation model and business information.
Business recommended unit, for according to association relation model, calculates the recommendation degree of each business of recommending to user, and according to user basic information by the high service propelling of recommendation degree to user.
The business recommended system of the present invention is arranged in cloud computing resource pool, and cloud computing resource pool itself comprises calculating and the storage resources that is distributed in diverse geographic location, like this, under cloud computing framework, by the scheduling strategy of cloud computing resources Management Unit, for the different demands of personalized service recommendation system related application, dynamic, transparent its required calculating and storage resources that provide, and when current application program is not used, its resource dynamic is reclaimed.That is to say, the present invention is based in the system framework of cloud computing resource pool, realized elasticity, the dynamic expansion of resource, shortened system response time, also promoted user's experience simultaneously.
Although the disclosed embodiment of the present invention as above, the embodiment that described content only adopts for ease of understanding the present invention, not in order to limit the present invention.Those of skill in the art under any the present invention; do not departing under the prerequisite of the disclosed spirit and scope of the present invention; can in the form of implementing and details, carry out any modification and variation; but scope of patent protection of the present invention, still must be as the criterion with the scope that appending claims was defined.
Claims (10)
1. a business recommended method, is characterized in that, comprising: obtain the raw information that user basic information, user are used internet, and user uses the position context information of mobile service;
According to the user who obtains, use the raw information of internet to set up user interest model; According to the user who obtains, use the position context information of mobile service to set up user situation model;
Set up the association relation model between user interest model, user situation model and business information, according to association relation model, calculate and to recommend the recommendation degree of new business to user, and by the high service propelling of recommendation degree to user.
2. business recommended method according to claim 1, is characterized in that, described user basic information at least comprises: subscriber identity information, order business information;
It is URL log information that described user uses the raw information of internet.
3. business recommended method according to claim 2, is characterized in that, the described user interest model of setting up comprises:
By the analysis to described URL log information, obtain the subject categories of user's accessed web page document;
Set up the corresponding relation of different category of interest and interest-degree;
Wherein, the class web document number that described interest-degree comprises with corresponding category of interest is directly proportional, and the mistiming of the web document comprising with the category of interest of nearest reading is inversely proportional to.
4. according to the business recommended method described in claim 2 or 3, it is characterized in that, the method also comprises: according to the timing length setting in advance, upgrade described user interest model.
5. business recommended method according to claim 1, is characterized in that, described user uses the position context information of mobile service to comprise: positional information, temporal information, terminal device information and business information.
6. business recommended method according to claim 5, is characterized in that, the described user situation model of setting up comprises:
According to different position scenario type, set up position, time, terminal, business, and the corresponding relation between the interest-degree of position sight.
7. according to the business recommended method described in claim 3 or 6, it is characterized in that, the described association relation model of setting up between user interest model, user situation model and business information comprises:
Calculate described each user's user interest model and the vector product of user situation modularity, to obtain association relation model.
8. business recommended method according to claim 7, is characterized in that, described the high service propelling of recommendation degree is comprised to user:
According to described association relation model, calculate and recommend the recommendation degree of new business to user;
The recommendation degree of new business is sorted, and according to the business recommended amount threshold of systemic presupposition, the service propelling that the recommendation degree of business is arranged in front to business recommended amount threshold item is to user.
9. a business recommended system, is characterized in that, at least comprises that acquiring unit, first sets up unit, second and set up unit, the 3rd and set up unit, and business recommended unit; Wherein,
Acquisition module, use the raw information of internet, and user uses the position context information of mobile service for user basic information, user;
First sets up unit, for using the raw information of internet to set up user interest model according to the user who obtains;
Second sets up unit, for using the position context information of mobile service to set up user situation model according to the user who obtains;
The 3rd sets up unit, for according to user interest model and the user situation model set up, sets up the association relation model between user interest model, user situation model and business information;
Business recommended unit, for according to association relation model, calculates the recommendation degree of each business of recommending to user, and according to user basic information by the high service propelling of recommendation degree to user.
10. business recommended system according to claim 9, is characterized in that, described business recommended system is arranged in cloud computing resource pool.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008184A (en) * | 2014-06-10 | 2014-08-27 | 百度在线网络技术（北京）有限公司 | Method and device for pushing information |
| CN104063457A (en) * | 2014-06-25 | 2014-09-24 | 北京智谷睿拓技术服务有限公司 | Information communication method, system and terminal |
| CN104168587A (en) * | 2014-08-11 | 2014-11-26 | 无锡高联信息技术有限公司 | Resource utilization method based on LTE system |
| CN104680399A (en) * | 2015-03-13 | 2015-06-03 | 江苏物联网研究发展中心 | Sale recommending method for agricultural material commodities |
| WO2016004763A1 (en) * | 2014-07-11 | 2016-01-14 | 华为技术有限公司 | Service recommendation method and device having intelligent assistant |
| CN105956009A (en) * | 2016-04-21 | 2016-09-21 | 深圳前海大数点科技有限公司 | Method for matching and pushing real-time scene content |
| CN106095887A (en) * | 2016-06-07 | 2016-11-09 | 兰州大学 | Context aware Web service recommendation method based on weighted space-time effect |
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| CN107086922A (en) * | 2016-02-15 | 2017-08-22 | 中国移动通信集团福建有限公司 | A kind of user behavior recognition method and apparatus |
| CN107332807A (en) * | 2016-04-29 | 2017-11-07 | 高德信息技术有限公司 | A kind of information-pushing method and device |
| US9912773B2 (en) | 2016-05-25 | 2018-03-06 | New Asia Technology Development Limited | Systems and methods to prioritize and schedule notifications with user behaviour and contextual data analysis |
| CN108280183A (en) * | 2018-01-23 | 2018-07-13 | 余绍志 | A kind of information transmission system based on big data matching and GPS positioning |
| CN109064268A (en) * | 2018-07-20 | 2018-12-21 | 中国建设银行股份有限公司 | Business recommended method, apparatus, server-side and storage medium |
| CN109657154A (en) * | 2018-12-28 | 2019-04-19 | 浙江省公众信息产业有限公司 | Resource collator and resource ordering method based on scene |
| CN109829108A (en) * | 2019-01-28 | 2019-05-31 | 北京三快在线科技有限公司 | Information recommendation method, device, electronic equipment and readable storage medium storing program for executing |
| CN112395486A (en) * | 2019-08-12 | 2021-02-23 | 中国移动通信集团重庆有限公司 | Broadband service recommendation method, system, server and storage medium |
| US11222273B2 (en) * | 2018-10-25 | 2022-01-11 | Advanced New Technologies Co., Ltd. | Service recommendation method, apparatus, and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090307262A1 (en) * | 2008-06-05 | 2009-12-10 | Samsung Electronics Co., Ltd. | Situation-dependent recommendation based on clustering |
| CN101923545A (en) * | 2009-06-15 | 2010-12-22 | 北京百分通联传媒技术有限公司 | Method for recommending personalized information |
| CN102196356A (en) * | 2010-03-08 | 2011-09-21 | 株式会社Ntt都科摩 | Method and system for recommending mobile services in communication network |
| CN102215300A (en) * | 2011-05-24 | 2011-10-12 | 中国联合网络通信集团有限公司 | Telecommunication service recommendation method and system |
| CN102421062A (en) * | 2011-12-01 | 2012-04-18 | 中国联合网络通信集团有限公司 | Method and system for pushing application information |
-
2013
- 2013-12-04 CN CN201310648021.XA patent/CN103617547B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090307262A1 (en) * | 2008-06-05 | 2009-12-10 | Samsung Electronics Co., Ltd. | Situation-dependent recommendation based on clustering |
| CN101923545A (en) * | 2009-06-15 | 2010-12-22 | 北京百分通联传媒技术有限公司 | Method for recommending personalized information |
| CN102196356A (en) * | 2010-03-08 | 2011-09-21 | 株式会社Ntt都科摩 | Method and system for recommending mobile services in communication network |
| CN102215300A (en) * | 2011-05-24 | 2011-10-12 | 中国联合网络通信集团有限公司 | Telecommunication service recommendation method and system |
| CN102421062A (en) * | 2011-12-01 | 2012-04-18 | 中国联合网络通信集团有限公司 | Method and system for pushing application information |
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| US10949477B2 (en) | 2014-07-11 | 2021-03-16 | Huawei Technologies Co., Ltd. | Service recommendation method and apparatus with intelligent assistant |
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| CN106095887A (en) * | 2016-06-07 | 2016-11-09 | 兰州大学 | Context aware Web service recommendation method based on weighted space-time effect |
| CN106852187A (en) * | 2016-06-28 | 2017-06-13 | 深圳狗尾草智能科技有限公司 | A kind of technical ability bag recommendation apparatus and method based on user's portrait |
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| CN112395486B (en) * | 2019-08-12 | 2023-11-03 | 中国移动通信集团重庆有限公司 | Broadband service recommendation method, system, server and storage medium |
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