CN104504133A - Application program recommending method and device - Google Patents
Application program recommending method and device Download PDFInfo
- Publication number
- CN104504133A CN104504133A CN201410855660.8A CN201410855660A CN104504133A CN 104504133 A CN104504133 A CN 104504133A CN 201410855660 A CN201410855660 A CN 201410855660A CN 104504133 A CN104504133 A CN 104504133A
- Authority
- CN
- China
- Prior art keywords
- application program
- information
- recommended
- similar users
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Abstract
The invention provides an application program recommending method and device. The method includes receiving identification information of current application program and acquiring similar user information of login users of the current application program from a server of the current application program according to the identification information of the current application program; acquiring the application program to be recommended according to the similar user information and the preset strategy; returning the application program to be recommended to a client to enable the client to display the application program to be recommended. By means of the application program recommending method and device, the application program can be recommended to current users in a pointed mode, the personal requirements of the users are met, and the recommendation accuracy is improved.
Description
Technical field
The present invention relates to field of computer technology, particularly relate to a kind of recommend method and device of application program.
Background technology
Along with mobile terminal (such as smart mobile phone, panel computer etc.) and the development of development of Mobile Internet technology, various mobile terminal application becomes increasingly abundant, mobile terminal manufacturer, mobile operator and internet manufacturer etc. have released one after another and have comprised the application shop of the terminal applies that it provides, as the App (application program that Apple company provides, Application) Store, Baidu's Mobile solution that Baidu provides, the Google Play etc. that Google company provides, a large amount of mobile terminal application is provided in these application shops, facilitate mobile phone users and search its mobile terminal application needed rapidly, reduce the time that user searches mobile terminal application.
At present, application shop is showing the mobile terminal application that different application provider provides while according to application class (video, music, navigation and social activity), can be the mobile terminal application that user shows recommendation according to the trigger action of user, but, there is following problem in the mode of the mobile terminal application that existing application shop pushes: after different users clicks recommendation, the content that application shop is recommended is identical, specific aim is not strong, can not meet the individual demand of user, recommendation effect is not good.
Summary of the invention
The present invention is intended to solve one of technical matters in correlation technique at least to a certain extent.For this reason, first aspect present invention embodiment is the recommend method proposing a kind of application program, and the method to active user's exemplary application program, meets the individual demand of user targetedly, improves the accuracy of recommendation.
A second aspect of the present invention embodiment is the recommendation apparatus proposing a kind of application program.
To achieve these goals, the recommend method of the application program of first aspect present invention embodiment, comprise: receive current application program identification information, and from the server of current application program, obtain the similar users information of the login user of current application program according to described current application program identification information; Application program to be recommended is obtained according to described similar users information and preset strategy; And return described application program to be recommended to client, represent described application program to be recommended to make described client.
According to the recommend method of the application program of the embodiment of the present invention, receive current application program identification information, and from the server of current application program, the similar users information of the login user of current application program is obtained according to current application program identification information, then application program to be recommended is obtained according to similar users information and preset strategy, and return application program to be recommended to client, client represents corresponding application program to be recommended, thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
To achieve these goals, the recommendation apparatus of the application program of second aspect present invention embodiment, comprise: acquisition module, for receiving current application program identification information, and from the server of current application program, obtain the similar users information of the login user of current application program according to described current application program identification information; Processing module, for obtaining application program to be recommended according to described similar users information and preset strategy; And return module, for returning described application program to be recommended to client, represent described application program to be recommended to make described client.
According to the recommendation apparatus of the application program of the embodiment of the present invention, current application program identification information is received by acquisition module, and from the server of current application program, the similar users information of the login user of current application program is obtained according to current application program identification information, and obtain application program to be recommended by processing module according to similar users information and preset strategy, and return application program to be recommended by returning module to client, client represents corresponding application program to be recommended, thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the recommend method of application program according to an embodiment of the invention.
Fig. 2 is the process flow diagram of the recommend method of application program in accordance with another embodiment of the present invention.
Fig. 3 is the structural representation of the recommendation apparatus of application program according to an embodiment of the invention.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Be exemplary below by the embodiment be described with reference to the drawings, be intended to for explaining the present invention, and can not limitation of the present invention be interpreted as.
Below with reference to the accompanying drawings recommend method and the device of the application program of the embodiment of the present invention are described.
Fig. 1 is the process flow diagram of the recommend method of application program according to an embodiment of the invention, and as shown in Figure 1, the recommend method of this application program comprises:
S101, receives current application program identification information, and from the server of current application program, obtains the similar users information of the login user of current application program according to current application program identification information.
Wherein, current application program is the application program of social class, and such as, current application program can be the application programs such as micro-letter, microblogging, QQ.
In addition, it should be noted that, the client in subsequent descriptions is to provide the application shop of terminal applies such as mobile phone application, and such as, client can be Baidu's Mobile solution client.
Such as, the account of micro-letter is used to log in application shop user, or after the social information of micro-letter can be accessed in subscriber authorisation application shop, server receives micro-letter identification information, and obtains the similar users information of current login user according to micro-letter identification information from the server of micro-letter.
S102, obtains application program to be recommended according to similar users information and preset strategy.
In this embodiment, after the similar users information obtaining active user, the information of the application program of similar users can be obtained according to similar users information from the server of current application program, and from the information of application program, obtain application program to be recommended based on preset strategy.
Particularly, the information of the information of the application program that similar users is recommended in current application program and the application program of similar users download can be obtained from the server of current application program.
Such as, assuming that current application program is micro-letter, in order to accurate exemplary application program, after the similar users obtaining active user, by the information of mode application program of similar users and friend recommendation from social circle of intelligent semantic identification, in addition, can also from the information obtaining the application program that similar users is downloaded application shop.It should be noted that, the application program that user recommends in social circle is also the application program downloaded from application shop.
In order to can accurately to active user's exemplary application program, after the information of application program of recommendation obtaining similar users and the information of the application program of download, can according to the recommendation number of times of the corresponding application program of information acquisition of the information of application program of recommending and the application program of download and download time, and use recommendation number of times and the download time of the corresponding application program of different weight factor weightings, then the result after weighting is sorted, and obtain the application program to be recommended being no more than predetermined quantity according to ranking results.Wherein, predetermined quantity is the value pre-set in server, and such as, predetermined quantity can be 4.
Such as, assuming that from six application programs of information acquisition of the information of application program of recommending and the application program of download, this six application programs are represented respectively with APP1, APP2, APP3, APP4, APP5, APP6, download time corresponding to these six application programs is respectively 10,20,15,12,25,30, and corresponding recommendation number of times is respectively 2,10,7,13,20,25.If weight factor corresponding to the download time set in server is 0.7, the weight factor of recommending number of times corresponding is 0.3, then the result after can obtaining APP1, APP2, APP3, APP4, APP5, APP6 weighting by calculating is 7.6,17,12.6,12.3,23.5,28.5.After the above-mentioned weighted results of acquisition, can sort according to order from big to small to the result after weighting, the order of application program corresponding after sequence is APP6, APP5, APP2, APP3, APP4, APP1.If the predetermined number set in server is 4, can obtain corresponding application program to be recommended is APP6, APP5, APP2 and APP3.
S103, returns application program to be recommended to client, represents application program to be recommended to make client.
Particularly, after server obtains application program to be recommended, application program to be recommended can be returned to client, the application program to be recommended that client reception server returns, if receive the recommendation button in user's trigger clients interface in client, client can represent application program to be recommended in the form of a list on interface.Thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
The recommend method of the application program of the embodiment of the present invention, receives current application program identification information, and from the server of current application program, obtains the similar users information of the login user of current application program according to current application program identification information; Then application program to be recommended is obtained according to similar users information and preset strategy, and return application program to be recommended to client, client represents corresponding application program to be recommended, thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
Fig. 2 is the process flow diagram of the recommend method of application program in accordance with another embodiment of the present invention, and this embodiment take current application program as microblogging, and client is that example is described for applying shop, and as shown in Figure 2, the recommend method of this application program comprises:
S201, receives microblogging identification information, and from the server of microblogging, obtains the similar users information of the login user of microblogging according to microblogging identification information.
Particularly, the account of microblogging is used to log in application shop user, or subscriber authorisation application is after shop can access the social information of microblogging, server receives microblogging identification information, and from server corresponding to microblogging, obtains the similar users information of current login user according to microblogging identification information.
S202, obtains application program to be recommended according to similar users information and preset strategy.
In this embodiment, after the similar users information obtaining current login user, the information of the application program of similar users can be obtained according to similar users information from server corresponding to microblogging, and from the information of application program, obtain application program to be recommended based on preset strategy.
Particularly, the information of the information of the application program that similar users is recommended in microblogging and the application program of similar users download can be obtained from the server of microblogging.
Specifically, after the similar users obtaining current login user, by the information of mode application program of similar users and friend recommendation from social circle of intelligent semantic identification, in addition, can also from the information obtaining the application program that similar users is downloaded application shop.
In order to can accurately to active user's exemplary application program, after the information of application program of recommendation obtaining similar users and the information of the application program of download, for the application program of each similar users, recommendation number of times and the download time of corresponding application program can be obtained, for recommending number of times and download time to arrange corresponding weight factor, and the weight factor arranged is used to compute weighted to the recommendation number of times of corresponding application program and download time; And sort based on all application programs of operation result to corresponding similar users, and obtain the application program to be recommended being no more than predetermined quantity according to ranking results.Wherein, predetermined quantity is the value pre-set in server, and such as, predetermined quantity can be 4.
S203, returns application program to be recommended to application shop, represents application program to be recommended to make application shop.
Particularly, after server obtains application program to be recommended, application program to be recommended can be returned to application shop, the application program to be recommended that application shop reception server returns, if receive user in application shop to trigger the recommendation button applied in store interface, application shop can represent application program to be recommended in the form of a list on interface.Thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
The recommend method of the application program of the embodiment of the present invention, receives current application program identification information, and from the server of current application program, obtains the similar users information of the login user of current application program according to current application program identification information; Then application program to be recommended is obtained according to similar users information and preset strategy, and return application program to be recommended to client, client represents corresponding application program to be recommended, thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
In order to realize above-described embodiment, the present invention also proposes a kind of recommendation apparatus of application program.
Fig. 3 is the structural representation of the recommendation apparatus of application program according to an embodiment of the invention.As shown in Figure 3, the recommendation apparatus of this application program comprises: acquisition module 100, processing module 200 and return module 300, wherein:
Acquisition module 100 for receiving current application program identification information, and obtains the similar users information of the login user of current application program from the server of current application program according to current application program identification information; Processing module 200 is for obtaining application program to be recommended according to similar users information and preset strategy; And return module 300 for returning application program to be recommended to client, represent application program to be recommended to make client.
Wherein, current application program is the application program of social class, and such as, current application program can be the application programs such as micro-letter, microblogging, QQ, and above-mentioned client is the application program of application shop class, and such as, above-mentioned client can be Baidu's mobile shop.
Above-mentioned acquisition module 100 is specifically for the information obtaining the application program of similar users according to similar users information from the server of current application program; And from the information of application program, obtain application program to be recommended based on preset strategy.
Particularly, acquisition module 100 can obtain the information of the application program that similar users is recommended in current application program; And/or obtain the information of the application program that similar users is downloaded.
Above-mentioned processing module 200 specifically for: for the application program of each similar users, obtain recommendation number of times and the download time of corresponding application program, for recommending number of times and download time to arrange corresponding weight factor, and the weight factor arranged is used to compute weighted to the recommendation number of times of corresponding application program and download time; And sort based on all application programs of operation result to corresponding similar users, and obtain the application program to be recommended being no more than predetermined quantity according to ranking results.
Wherein, predetermined quantity presets the value arranged in server, and such as, predetermined quantity is 4.The weight factor of above-mentioned recommendation number of times and download time also can pre-set, such as, if think, the application program that similar users is recommended is important, the weight factor of the recommendation number of times of correspondence can be set to 0.7, the weight factor of download time is set to 0.3, assuming that download time corresponding to APP1 is 10, corresponding recommendation number of times is 2, then be weighted the recommendation number of times of APP1 and download time, the result of corresponding weighting is 7.6, i.e. 0.7*10+2*0.3.
The process of recommendation apparatus exemplary application program of the application program comprising acquisition module 100, processing module 200 and return module 300 see the text description of Fig. 1 and correspondence thereof, can not repeat herein.
The recommendation apparatus of the application program of the embodiment of the present invention, current application program identification information is received by acquisition module, and from the server of current application program, the similar users information of the login user of current application program is obtained according to current application program identification information, and obtain application program to be recommended by processing module according to similar users information and preset strategy, and return application program to be recommended by returning module to client, client represents corresponding application program to be recommended, thus, targetedly to active user's exemplary application program, meet the individual demand of user, improve the accuracy of recommendation.
In the description of this instructions, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, to the schematic representation of above-mentioned term not must for be identical embodiment or example.And the specific features of description, structure, material or feature can combine in one or more embodiment in office or example in an appropriate manner.In addition, when not conflicting, the feature of the different embodiment described in this instructions or example and different embodiment or example can carry out combining and combining by those skilled in the art.
In addition, term " first ", " second " only for describing object, and can not be interpreted as instruction or hint relative importance or imply the quantity indicating indicated technical characteristic.Thus, be limited with " first ", the feature of " second " can express or impliedly comprise at least one this feature.In describing the invention, the implication of " multiple " is at least two, such as two, three etc., unless otherwise expressly limited specifically.
Describe and can be understood in process flow diagram or in this any process otherwise described or method, represent and comprise one or more for realizing the module of the code of the executable instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiment of the present invention comprises other realization, wherein can not according to order that is shown or that discuss, comprise according to involved function by the mode while of basic or by contrary order, carry out n-back test, this should understand by embodiments of the invention person of ordinary skill in the field.
In flow charts represent or in this logic otherwise described and/or step, such as, the sequencing list of the executable instruction for realizing logic function can be considered to, may be embodied in any computer-readable medium, for instruction execution system, device or equipment (as computer based system, comprise the system of processor or other can from instruction execution system, device or equipment instruction fetch and perform the system of instruction) use, or to use in conjunction with these instruction execution systems, device or equipment.With regard to this instructions, " computer-readable medium " can be anyly can to comprise, store, communicate, propagate or transmission procedure for instruction execution system, device or equipment or the device that uses in conjunction with these instruction execution systems, device or equipment.The example more specifically (non-exhaustive list) of computer-readable medium comprises following: the electrical connection section (electronic installation) with one or more wiring, portable computer diskette box (magnetic device), random access memory (RAM), ROM (read-only memory) (ROM), erasablely edit ROM (read-only memory) (EPROM or flash memory), fiber device, and portable optic disk ROM (read-only memory) (CDROM).In addition, computer-readable medium can be even paper or other suitable media that can print described program thereon, because can such as by carrying out optical scanning to paper or other media, then carry out editing, decipher or carry out process with other suitable methods if desired and electronically obtain described program, be then stored in computer memory.
Should be appreciated that each several part of the present invention can realize with hardware, software, firmware or their combination.In the above-described embodiment, multiple step or method can with to store in memory and the software performed by suitable instruction execution system or firmware realize.Such as, if realized with hardware, the same in another embodiment, can realize by any one in following technology well known in the art or their combination: the discrete logic with the logic gates for realizing logic function to data-signal, there is the special IC of suitable combinational logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) etc.
Those skilled in the art are appreciated that realizing all or part of step that above-described embodiment method carries is that the hardware that can carry out instruction relevant by program completes, described program can be stored in a kind of computer-readable recording medium, this program perform time, step comprising embodiment of the method one or a combination set of.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, also can be that the independent physics of unit exists, also can be integrated in a module by two or more unit.Above-mentioned integrated module both can adopt the form of hardware to realize, and the form of software function module also can be adopted to realize.If described integrated module using the form of software function module realize and as independently production marketing or use time, also can be stored in a computer read/write memory medium.
The above-mentioned storage medium mentioned can be ROM (read-only memory), disk or CD etc.Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, and those of ordinary skill in the art can change above-described embodiment within the scope of the invention, revises, replace and modification.
Claims (8)
1. a recommend method for application program, is characterized in that, comprising:
Receive current application program identification information, and from the server of current application program, obtain the similar users information of the login user of described current application program according to described current application program identification information;
Application program to be recommended is obtained according to described similar users information and preset strategy; And
Return described application program to be recommended to client, represent described application program to be recommended to make described client.
2. method according to claim 1, is characterized in that, described according to described similar users information and preset strategy obtain application program to be recommended, comprising:
From the server of current application program, the information of the application program of similar users is obtained according to described similar users information; And
From the information of described application program, described application program to be recommended is obtained based on described preset strategy.
3. method according to claim 2, is characterized in that, the described information obtaining the application program of similar users according to described similar users information from the server of current application program, comprising:
Obtain the information of the application program that described similar users is recommended in described current application program; With
Obtain the information of the application program that described similar users is downloaded.
4. method according to claim 3, is characterized in that, describedly from the information of described application program, obtains described application program to be recommended based on described preset strategy, comprising:
For the application program of each similar users, obtain recommendation number of times and the download time of corresponding application program, for described recommendation number of times and described download time arrange corresponding weight factor, and the weight factor arranged is used to compute weighted to the recommendation number of times of corresponding application program and download time; And
Sort based on all application programs of operation result to corresponding similar users, and obtain the application program to be recommended being no more than predetermined quantity according to ranking results.
5. a recommendation apparatus for application program, is characterized in that, comprising:
Acquisition module, for receiving current application program identification information, and obtains the similar users information of the login user of described current application program from the server of current application program according to described current application program identification information;
Processing module, for obtaining application program to be recommended according to described similar users information and preset strategy; And
Return module, for returning described application program to be recommended to client, represent described application program to be recommended to make described client.
6. device according to claim 5, is characterized in that, described acquisition module, specifically for:
From the server of current application program, the information of the application program of similar users is obtained according to described similar users information; And
From the information of described application program, described application program to be recommended is obtained based on described preset strategy.
7. device according to claim 6, is characterized in that, described acquisition module, specifically for:
Obtain the information of the application program that described similar users is recommended in described current application program; With
Obtain the information of the application program that described similar users is downloaded.
8. device according to claim 7, is characterized in that, described processing module, specifically for:
For the application program of each similar users, obtain recommendation number of times and the download time of corresponding application program, for described recommendation number of times and described download time arrange corresponding weight factor, and the weight factor arranged is used to compute weighted to the recommendation number of times of corresponding application program and download time; And
Sort based on all application programs of operation result to corresponding similar users, and obtain the application program to be recommended being no more than predetermined quantity according to ranking results.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410855660.8A CN104504133B (en) | 2014-12-31 | 2014-12-31 | The recommendation method and device of application program |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410855660.8A CN104504133B (en) | 2014-12-31 | 2014-12-31 | The recommendation method and device of application program |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104504133A true CN104504133A (en) | 2015-04-08 |
| CN104504133B CN104504133B (en) | 2018-01-09 |
Family
ID=52945530
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410855660.8A Active CN104504133B (en) | 2014-12-31 | 2014-12-31 | The recommendation method and device of application program |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104504133B (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105630977A (en) * | 2015-12-25 | 2016-06-01 | 腾讯科技（深圳）有限公司 | Application recommending method, device and system |
| CN106326297A (en) * | 2015-07-01 | 2017-01-11 | 北京搜狗科技发展有限公司 | Application recommendation method and device |
| CN106960367A (en) * | 2017-03-31 | 2017-07-18 | 北京猎豹移动科技有限公司 | Promotion method, device and the server of application program |
| CN107357657A (en) * | 2017-06-27 | 2017-11-17 | 北京小蓦机器人技术有限公司 | A kind of method and apparatus for being used to provide the sub- APP channels of target in application |
| CN107798100A (en) * | 2017-10-30 | 2018-03-13 | 江西博瑞彤芸科技有限公司 | A kind of collocation method using recommendation information |
| CN107807944A (en) * | 2016-09-09 | 2018-03-16 | 北京搜狗科技发展有限公司 | A kind of file recommendation method, device and electronic equipment |
| CN108958826A (en) * | 2017-05-22 | 2018-12-07 | 北京京东尚科信息技术有限公司 | The method and apparatus of dynamic configuration application installation package |
| CN109756868A (en) * | 2017-11-01 | 2019-05-14 | 中国移动通信集团终端有限公司 | Recommended method, device, equipment and the medium of application program |
| CN110188277A (en) * | 2019-05-31 | 2019-08-30 | 苏州百智通信息技术有限公司 | A kind of recommended method and device of resource |
| CN111199491A (en) * | 2018-10-31 | 2020-05-26 | 百度在线网络技术（北京）有限公司 | Social circle recommendation method and device |
| CN111831889A (en) * | 2019-04-15 | 2020-10-27 | 泰康保险集团股份有限公司 | Block chain-based virtual fitness application recommendation method and device |
| CN112131476A (en) * | 2020-09-27 | 2020-12-25 | 深圳市锐尔觅移动通信有限公司 | Application recommendation method, device, apparatus, terminal and readable storage medium |
| CN112256956A (en) * | 2020-09-17 | 2021-01-22 | 北京一亩田新农网络科技有限公司 | Information recommendation method and information recommendation device |
| CN113347493A (en) * | 2021-06-01 | 2021-09-03 | 广州欢网科技有限责任公司 | Third-party application personalized delivery and installation method and system suitable for television terminal |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102253858A (en) * | 2011-06-27 | 2011-11-23 | 宇龙计算机通信科技(深圳)有限公司 | Device and method for managing application programs |
| CN102567511A (en) * | 2011-12-27 | 2012-07-11 | 奇智软件（北京）有限公司 | Method and device for automatically recommending application |
| US20130311612A1 (en) * | 2012-05-16 | 2013-11-21 | Rackspace Us, Inc. | Indirection Objects in a Cloud Storage System |
| US20140181074A1 (en) * | 2012-12-20 | 2014-06-26 | Teradata Corporation | Estimating number of iterations or self joins required to evaluate iterative or recursive database queries |
| CN103902729A (en) * | 2014-04-14 | 2014-07-02 | 广州金山网络科技有限公司 | Method and device for recommending application program |
| CN104239523A (en) * | 2014-09-17 | 2014-12-24 | 北京金山安全软件有限公司 | Recommendation method and device for application program and mobile terminal |
-
2014
- 2014-12-31 CN CN201410855660.8A patent/CN104504133B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102253858A (en) * | 2011-06-27 | 2011-11-23 | 宇龙计算机通信科技(深圳)有限公司 | Device and method for managing application programs |
| CN102567511A (en) * | 2011-12-27 | 2012-07-11 | 奇智软件（北京）有限公司 | Method and device for automatically recommending application |
| US20130311612A1 (en) * | 2012-05-16 | 2013-11-21 | Rackspace Us, Inc. | Indirection Objects in a Cloud Storage System |
| US20140181074A1 (en) * | 2012-12-20 | 2014-06-26 | Teradata Corporation | Estimating number of iterations or self joins required to evaluate iterative or recursive database queries |
| CN103902729A (en) * | 2014-04-14 | 2014-07-02 | 广州金山网络科技有限公司 | Method and device for recommending application program |
| CN104239523A (en) * | 2014-09-17 | 2014-12-24 | 北京金山安全软件有限公司 | Recommendation method and device for application program and mobile terminal |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326297A (en) * | 2015-07-01 | 2017-01-11 | 北京搜狗科技发展有限公司 | Application recommendation method and device |
| CN105630977B (en) * | 2015-12-25 | 2019-03-26 | 腾讯科技（深圳）有限公司 | Application program recommended method, apparatus and system |
| CN105630977A (en) * | 2015-12-25 | 2016-06-01 | 腾讯科技（深圳）有限公司 | Application recommending method, device and system |
| CN107807944A (en) * | 2016-09-09 | 2018-03-16 | 北京搜狗科技发展有限公司 | A kind of file recommendation method, device and electronic equipment |
| CN106960367A (en) * | 2017-03-31 | 2017-07-18 | 北京猎豹移动科技有限公司 | Promotion method, device and the server of application program |
| CN108958826A (en) * | 2017-05-22 | 2018-12-07 | 北京京东尚科信息技术有限公司 | The method and apparatus of dynamic configuration application installation package |
| CN108958826B (en) * | 2017-05-22 | 2022-06-07 | 北京京东尚科信息技术有限公司 | Method and device for dynamically configuring application installation package |
| CN107357657A (en) * | 2017-06-27 | 2017-11-17 | 北京小蓦机器人技术有限公司 | A kind of method and apparatus for being used to provide the sub- APP channels of target in application |
| CN107357657B (en) * | 2017-06-27 | 2020-07-17 | 北京小蓦机器人技术有限公司 | Method and equipment for providing target sub APP channel in application |
| CN107798100A (en) * | 2017-10-30 | 2018-03-13 | 江西博瑞彤芸科技有限公司 | A kind of collocation method using recommendation information |
| CN109756868B (en) * | 2017-11-01 | 2022-01-04 | 中国移动通信集团终端有限公司 | Recommendation method, device, equipment and medium for application program |
| CN109756868A (en) * | 2017-11-01 | 2019-05-14 | 中国移动通信集团终端有限公司 | Recommended method, device, equipment and the medium of application program |
| CN111199491A (en) * | 2018-10-31 | 2020-05-26 | 百度在线网络技术（北京）有限公司 | Social circle recommendation method and device |
| CN111199491B (en) * | 2018-10-31 | 2023-08-22 | 百度在线网络技术（北京）有限公司 | Social circle recommendation method and device |
| CN111831889A (en) * | 2019-04-15 | 2020-10-27 | 泰康保险集团股份有限公司 | Block chain-based virtual fitness application recommendation method and device |
| CN110188277A (en) * | 2019-05-31 | 2019-08-30 | 苏州百智通信息技术有限公司 | A kind of recommended method and device of resource |
| CN112256956A (en) * | 2020-09-17 | 2021-01-22 | 北京一亩田新农网络科技有限公司 | Information recommendation method and information recommendation device |
| CN112131476A (en) * | 2020-09-27 | 2020-12-25 | 深圳市锐尔觅移动通信有限公司 | Application recommendation method, device, apparatus, terminal and readable storage medium |
| CN112131476B (en) * | 2020-09-27 | 2023-12-08 | 深圳市锐尔觅移动通信有限公司 | Application recommendation method, device, apparatus, terminal and readable storage medium |
| CN113347493A (en) * | 2021-06-01 | 2021-09-03 | 广州欢网科技有限责任公司 | Third-party application personalized delivery and installation method and system suitable for television terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104504133B (en) | 2018-01-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104504133A (en) | Application program recommending method and device | |
| US11036820B2 (en) | Page loading method and electronic device | |
| US20200065139A1 (en) | Task completion through inter-application communication | |
| US11216509B2 (en) | Dynamic faceting for personalized search and discovery | |
| US20150095267A1 (en) | Techniques to dynamically generate real time frequently asked questions from forum data | |
| US10387528B2 (en) | Search results integrated with interactive conversation service interface | |
| KR20190006383A (en) | Method for predicing purchase probability based on behavior sequence of user and apparatus therefor | |
| US9330186B2 (en) | Similarity engine for facilitating re-creation of an application collection of a source computing device on a destination computing device | |
| WO2013106708A1 (en) | Guided workflows for establishing a web presence | |
| CN104503975A (en) | Method and device for customizing recommended card | |
| CN109636485B (en) | Information recommendation method and device based on driving behavior data | |
| CN105491126A (en) | Service providing method and service providing device based on artificial intelligence | |
| CN108712478A (en) | A kind of method and apparatus for sharing boarding application | |
| US20130275456A1 (en) | Method and System for Content Search | |
| US11250468B2 (en) | Prompting web-based user interaction | |
| CN105045837A (en) | Information searching method and information searching device | |
| US20160125080A1 (en) | Accessing Special Purpose Search Systems | |
| CN103995846A (en) | Application message searching method and device | |
| CN109446415A (en) | A kind of application recommendation, acquisition methods and equipment | |
| CN105787053A (en) | Application pushing method and terminal | |
| US20180060964A1 (en) | Intelligent agent as competitor and collaborator in a system for addressing an enterprise opportunity | |
| CN103457944A (en) | Method and device for pushing information and server | |
| CN113326418A (en) | Method and device for determining webpage information source and webpage quality | |
| CN111177562B (en) | Recommendation ordering processing method and device for target object and server | |
| US20180025359A1 (en) | Customer journey optimized pre-fetching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |