CN111738768A - Advertisement pushing method and system - Google Patents
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Abstract
The embodiment of the application provides an advertisement pushing method and system, and relates to the technical field of advertisement pushing. The method comprises the following steps: the advertisement server obtains a user parameter set which is uploaded by a user terminal and is associated with a user clicking an advertisement position; the big data platform determines a target label matched with the user parameter set from a pre-stored label set according to the user parameter set; the advertisement server determines advertisements matched with the target tags according to the target tags; and the advertisement server pushes the advertisement to the user terminal. The advertisement pushing method and the advertisement pushing system can accurately deliver advertisements to users with different requirements in a targeted manner.
Description
Technical Field
The present document relates to the technical field of advertisement push, and in particular, to an advertisement push method and system.
Background
As internet technology becomes more and more sophisticated, users using the internet become more and more, and advertisement delivery is more and more widely applied to various applications.
At present, most of the advertisement pushing is the one-way propagation consultation information of users from the advertisement putting direction, and the one-way propagation of the full-looking advertisements cannot aim at accurately putting the advertisements to the users with different requirements, so that the users are difficult to resonate, even the put advertisements are subjected to the dislike emotion, and the advertisement effect is greatly reduced.
Therefore, how to provide an effective advertisement delivery scheme to accurately push advertisements for users with different requirements is an urgent problem in the prior art.
Disclosure of Invention
The embodiment of the application provides an advertisement pushing method, which is used for solving the problem that in the prior art, advertisements cannot be accurately delivered to users with different requirements in a targeted manner.
The embodiment of the application provides an advertisement pushing system, which is used for solving the problem that in the prior art, advertisements cannot be accurately put to users with different requirements in a targeted manner.
The embodiment of the application adopts the following technical scheme:
an advertisement push method comprising:
the advertisement server obtains a user parameter set which is uploaded by a user terminal and is associated with a user clicking an advertisement position;
the big data platform determines a target label matched with the user parameter set from a pre-stored label set according to the user parameter set;
the advertisement server determines an advertisement matched with the target label according to the target label;
and the advertisement server pushes the advertisement to the user terminal.
Optionally, the determining, by the big data platform, a target tag matched with the user parameter set from a pre-stored tag set according to the user parameter set includes:
and the big data platform determines at least one target label with the highest relevance degree with the user parameter set from a pre-stored label set according to the user parameter set.
Optionally, the determining, by the big data platform, at least one target tag with the highest degree of association with the user parameter set from a pre-stored tag set according to the user parameter set includes:
the big data platform determines the at least one target label according to a weight coefficient corresponding to a parameter type of a behavior parameter associated with a user behavior in the user parameter set, an attenuation coefficient corresponding to the behavior parameter, the occurrence frequency of the behavior parameter in a preset time period and a weight coefficient corresponding to the behavior parameter;
the attenuation coefficient corresponding to the behavior parameter is obtained by calculation based on a time attenuation function, and the weight coefficient corresponding to the behavior parameter is obtained by calculation based on a TF-IDF algorithm.
Optionally, the determining, by the advertisement server, the advertisement matched with the target tag according to the target tag includes:
and the advertisement server determines the advertisement with the most marking times of the target label according to the target label.
Optionally, the method further includes:
the advertisement server sends the user parameter set to the big data platform.
Optionally, the user parameter set includes an operation parameter corresponding to the advertisement click location of the user, a device parameter of the user terminal where the user logs in, and a user parameter of the user.
An advertisement pushing system comprises an advertisement server and a big data platform, wherein the advertisement server is used for obtaining a user parameter set which is uploaded by a user terminal and is associated with a user clicking an advertisement position;
the big data platform is used for determining a target label matched with the user parameter set from a pre-stored label set according to the user parameter set;
the advertisement server is further used for determining advertisements matched with the target tags according to the target tags;
the advertisement server is also used for pushing the advertisement to the user terminal.
Optionally, the big data platform is configured to determine, according to the user parameter set, at least one target tag with a highest degree of association with the user parameter set from a pre-stored tag set.
Optionally, the advertisement server is configured to determine, according to the target tag, an advertisement with the largest number of times of marking the target tag.
Optionally, the advertisement server is further configured to send the user parameter set to the big data platform.
The above-mentioned at least one technical scheme that this application one or more embodiments adopted can reach following beneficial effect:
the target label matched with the user is determined from the label set according to the user parameter set uploaded by the user terminal and associated with the user clicking the advertisement position, and the advertisement matched with the target label is determined according to the target label and pushed to the user terminal, so that the feedback of the user is considered, the advertisement is accurately delivered to the user, and the user experience and the advertisement delivery effect are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure in any way. In the drawings:
fig. 1 is a schematic application environment diagram of an advertisement push method and system according to an embodiment of the present application.
Fig. 2 is a flowchart of an advertisement push method according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an advertisement push system according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of this document more clear, the technical solutions of this document will be clearly and completely described below with reference to specific embodiments of this document and corresponding drawings. It is to be understood that the embodiments described are only a few embodiments of this document, and not all embodiments. All other embodiments obtained by a person skilled in the art without making creative efforts based on the embodiments in this document belong to the protection scope of this document.
In order to solve the problem that the advertisements cannot be accurately delivered to users with different requirements in a targeted manner, the embodiment of the application provides an advertisement delivery method and an advertisement delivery system, and the advertisement delivery method and the advertisement delivery system can give consideration to feedback of the users and accurately deliver the advertisements to the users with different requirements.
Fig. 1 is a schematic diagram of an application environment of the advertisement push method and system according to the embodiment of the present application. As shown in FIG. 1, the advertisement server 100 is communicatively coupled to the big data platform 200 and the user terminal 300 via a network 400 for data communication or interaction. The advertisement server 100 may be a web server, a database server, etc., the user terminal 300 may be, but is not limited to, a smart phone, a Personal Computer (PC), a tablet PC, a Personal Digital Assistant (PDA), etc., and the network 400 may be a wired or wireless network.
The advertisement push method provided by the embodiment of the present application will be described in detail below.
Specifically, the flow of the advertisement push method is shown in fig. 2, and may include the following steps:
step S201, the advertisement server obtains a user parameter set associated with a user clicking an advertisement slot, which is uploaded by the user terminal.
In the embodiment of the application, the user terminal is provided with an application capable of pushing advertisements to the user, such as an express delivery pickup application, a WeChat public number and the like. When a user logs in the application at the user terminal and clicks on an ad slot of an application page, the user terminal sends a set of user parameters associated with the user to an ad server.
Wherein an ad slot may be a location in an application page where an ad is exposed.
In this embodiment, the user parameter set may include an operation parameter corresponding to the advertisement slot clicked by the user, an equipment parameter of the user terminal where the user logs in, a user parameter of the user, and the like, and may further include some other parameters, such as a location parameter and the like.
For example, the parameters in the user parameter set may include at least one of parameters such as a user ID, an advertisement location, a clicked resource location (an advertisement link of a clicked advertisement), a bound mobile phone number, a login manner, a device type of a user terminal, a device model of the user terminal, an operating system of the user terminal, a city, a province, an access time, a user gender, and a user age, and when the application is an express pickup application, the user parameter set may further include at least one of parameters such as pickup data (such as pickup quantity, pickup frequency, and the like), a website ID, a pickup address, and the like.
It should be understood that the above is merely an example, and in some other embodiments, other parameters may also be included in the user parameter set, and the embodiments of the present application are not limited specifically.
In this embodiment, after obtaining the user parameter set uploaded by the user terminal 300, the advertisement server 100 may send the obtained user parameter set to the big data platform 200.
Step S202, the big data platform determines a target label matched with the user parameter set from a pre-stored label set according to the user parameter set.
The big data platform 200 is pre-stored with a tag set comprising a plurality of user tags, and the user tags in the tag set may be generated according to historical data of a large number of users or may be customized by advertisers. The user tags in the tag set may include a plurality of sub-tags characterizing the user characteristics, and the sub-tags in the user tags may correspond to the number of user parameters in the user parameter set one by one, for example, when the sub-tags in the user tags include a user ID, the user parameters in the user parameter set may also include the user ID. It will be appreciated that in other embodiments, the sub-tags in the user tag may also correspond to user parameter portions of the user parameter set.
The user tags in the tag set may include a plurality of sub-tags characterizing the user characteristics, for example, the sub-tags in the user tags may be at least one of a city, a recommended population (the number of people who recommend goods among users), a user purchasing behavior (such as whether to click on a goods advertisement for purchase), a purchasing amount, a purchasing time period, an advertisement type, a collected goods type, a browsing number, a gender, a user pickup (such as pickup frequency), an attention type, an age, an advertisement click location, a browsing article type, and the like, which is not specifically limited in this embodiment.
The number of target tags may be one or more, and when determining the target tags matching the user parameter set, the big data platform 200 may determine, from the pre-stored set of tags, at least one target tag having the highest association with the user parameter set according to the user parameter set.
In this embodiment, the big data platform 200 may determine the at least one target tag according to a weight coefficient corresponding to a parameter type of a behavior parameter associated with a user behavior in the user parameter set, an attenuation coefficient corresponding to the behavior parameter, the number of occurrences of the behavior parameter in a predetermined time period, and a weight coefficient corresponding to the behavior parameter. The attenuation coefficient corresponding to the behavior parameter is obtained by calculation based on a time attenuation function, and the weight coefficient corresponding to the behavior parameter is obtained by calculation based on a TF-IDF algorithm.
It is understood that, in some other embodiments, the big data platform 200 may also determine the at least one target tag according to a weight coefficient corresponding to a parameter type of each user parameter in the user parameter set, a decay coefficient corresponding to each user parameter, a number of occurrences of each user parameter in a predetermined time period, and a weight coefficient corresponding to each user parameter.
For example, the user parameter set includes a plurality of user parameters such as age, gender (which may be behavior parameters because age and gender have a relationship with the content of interest of the user and affect the behavior of the user), purchase time period, purchase amount, and type of collected goods. Similarly, the user tags include a plurality of sub-tags such as age, gender, purchase time period, purchase amount, and type of the collected goods. When the target tag is determined, the matching degree of the behavior parameters in the user parameter set and the corresponding sub-tag in each user tag can be calculated, the association degree (matching degree) between the user parameter set and each user tag is obtained, and then one or more user tags with the highest association degree are selected as the target tag.
In the embodiment of the application, the weight coefficient corresponding to the parameter type of each behavior parameter in the user parameter set can be set according to the operator of the advertisement delivery party, for example, the weight coefficient can be determined according to the complexity of the parameter, the weight coefficient corresponding to the parameter which can be directly known by age, gender and the like is low, and the weight coefficient corresponding to the parameter which needs to be counted, such as the purchase time period, the purchase amount, the type of the collected goods and the like is high.
The attenuation coefficient corresponding to each behavior parameter is calculated based on a time attenuation function, and may be determined according to the time when the same or similar behavior parameter is obtained last time, for example, the longer the distance time is, the lower the corresponding weight is, that is, the smaller the value corresponding to the attenuation coefficient is.
The more occurrences of the behavior parameter within the predetermined time period, the higher the corresponding weight coefficient.
The weight coefficient corresponding to the parameter is calculated based on a term Frequency-inverse text Frequency (TF-IDF) algorithm. In the embodiment of the present application, the formula for calculating the word frequency may be TF (P, T) ═ W (P, T)/Σ W (P, Ti), Σ W (P, Ti) indicates the total number of sub-tags in the user tag, and W (P, T) indicates the matching number of the user parameter of one user and the sub-tags in the user tag. The formula for calculating the inverse text frequency may be IDF (P, T) ═ Σ W (Pi, Ti)/Σ W (Pi, T), where Σ Σ W (Pi, Ti) may represent the sum of all tags corresponding to a plurality of users, and Σ W (Pi, T) represents the sum of the number of users whose behavior parameters match the sub-tag T. And obtaining the weight coefficient corresponding to the behavior parameter according to the product of the word frequency and the reverse text frequency.
Step S203, the advertisement server determines the advertisement matched with the target label according to the target label.
Specifically, the advertisement server 100 may determine the advertisement with the most marked target tag according to the target tag, and the advertisement may be one or more advertisements. The advertisement with the largest number of times of marking the target tag may be an advertisement with the largest number of times of marking the target tag within a certain time, where the certain time may be a day, a week, a month, or the like, and is not specifically limited in this embodiment of the application.
In the embodiment of the present application, the advertisement marked with the largest number of target tags may refer to that the advertisement is clicked the largest number of times by the user matching the target tags.
Step S204, the advertisement server pushes the advertisement to the user terminal.
In the embodiment of the application, when the advertisement is pushed to the user terminal, the advertisement link, the time for loading the publication, the time for unloading the publication and the like can be obtained according to the advertisement ID, and then the advertisement is pushed to the user terminal according to the advertisement link, the time for loading the publication and the time for unloading the publication.
The publication loading time refers to the starting time of pushing the advertisement to the user terminal, and the publication loading time refers to the time of stopping pushing the advertisement to the user terminal, namely, the advertisement can be pushed to the user terminal between the publication loading time and the publication unloading time.
The advertisement pushing method can determine the target label matched with the advertisement position from the label set according to the user parameter set uploaded by the user terminal and associated with the user clicking the advertisement position, determine the advertisement matched with the target label according to the target label and push the advertisement to the user terminal, so that the feedback of the user is considered, the advertisement is accurately delivered to the user, the user is prevented from generating negative emotion to the pushed advertisement information, and the user experience and the advertisement delivery effect are improved.
Fig. 3 is a schematic structural diagram of an advertisement push system according to an embodiment of the present application, where the advertisement push system includes an advertisement server 100 and a big data platform 200, and the advertisement server 100 is in communication connection with the big data platform 200.
The advertisement server 100 is configured to obtain a user parameter set uploaded by a user terminal and associated with a user clicking an advertisement slot.
In this embodiment, the user parameter set may include an operation parameter corresponding to the advertisement slot clicked by the user, an equipment parameter of the user terminal where the user logs in, a user parameter of the user, and the like, and may further include some other parameters, such as a location parameter and the like.
The big data platform 200 is configured to determine a target tag matching the user parameter set from a pre-stored tag set according to the user parameter set.
Specifically, the big data platform 200 is configured to determine, according to the user parameter set, at least one target tag with a highest degree of association with the user parameter set from a pre-stored tag set.
More specifically, the big data platform 200 is configured to determine the at least one target tag according to a weight coefficient corresponding to a parameter type of a behavior parameter associated with a user behavior in the user parameter set, an attenuation coefficient corresponding to the behavior parameter, a number of occurrences of the behavior parameter in a predetermined time period, and a weight coefficient corresponding to the behavior parameter. The attenuation coefficient corresponding to the behavior parameter is obtained by calculation based on a time attenuation function, and the weight coefficient corresponding to the behavior parameter is obtained by calculation based on a TF-IDF algorithm.
The advertisement server 100 is further configured to determine an advertisement matching the target tag according to the target tag.
Specifically, the advertisement server 100 determines the advertisement with the largest number of times of marking the target tag according to the target tag.
The advertisement server 100 is also used to push advertisements to the user terminal.
By adopting the device, the target label matched with the user can be determined from the label set according to the user parameter set uploaded by the user terminal and associated with the user clicking the advertisement position, and the advertisement matched with the target label is determined according to the target label and is pushed to the user terminal, so that the feedback of the user is considered, the advertisement is accurately delivered to the user, the phenomenon that the user generates a counter emotion to the pushed advertisement information is avoided, and the user experience and the advertisement delivery effect are improved.
The foregoing description of specific embodiments of the present application has been presented. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
In short, the above description is only a preferred embodiment of this document, and is not intended to limit the scope of protection of this document. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document shall be included in the protection scope of this document.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in this document are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (10)
1. An advertisement pushing method, comprising:
the advertisement server obtains a user parameter set which is uploaded by a user terminal and is associated with a user clicking an advertisement position;
the big data platform determines a target label matched with the user parameter set from a pre-stored label set according to the user parameter set;
the advertisement server determines an advertisement matched with the target label according to the target label;
and the advertisement server pushes the advertisement to the user terminal.
2. The method of claim 1, wherein the big data platform determines a target tag matching the user parameter set from a pre-stored set of tags according to the user parameter set, comprising:
and the big data platform determines at least one target label with the highest relevance degree with the user parameter set from a pre-stored label set according to the user parameter set.
3. The method of claim 2, wherein the big data platform determines, from a pre-stored set of tags, at least one target tag with a highest association with the set of user parameters according to the set of user parameters, comprising:
the big data platform determines the at least one target label according to a weight coefficient corresponding to a parameter type of a behavior parameter associated with a user behavior in the user parameter set, an attenuation coefficient corresponding to the behavior parameter, the occurrence frequency of the behavior parameter in a preset time period and a weight coefficient corresponding to the behavior parameter;
the attenuation coefficient corresponding to the behavior parameter is obtained by calculation based on a time attenuation function, and the weight coefficient corresponding to the behavior parameter is obtained by calculation based on a TF-IDF algorithm.
4. The method of claim 1, wherein the advertisement server determines the advertisement matching the target tag according to the target tag, comprising:
and the advertisement server determines the advertisement with the most marking times of the target label according to the target label.
5. The method of claim 1, further comprising:
the advertisement server sends the user parameter set to the big data platform.
6. The method of claim 1, wherein the set of user parameters includes an operation parameter corresponding to the user clicking an advertisement slot, a device parameter of the user terminal logged in by the user, and a user parameter of the user.
7. The advertisement pushing system is characterized by comprising an advertisement server and a big data platform, wherein the advertisement server is used for obtaining a user parameter set which is uploaded by a user terminal and is associated with a user clicking an advertisement position;
the big data platform is used for determining a target label matched with the user parameter set from a pre-stored label set according to the user parameter set;
the advertisement server is further used for determining advertisements matched with the target tags according to the target tags;
the advertisement server is also used for pushing the advertisement to the user terminal.
8. The advertisement push system according to claim 7, wherein the big data platform is configured to determine, according to the user parameter set, at least one target tag with a highest association degree with the user parameter set from a pre-stored tag set.
9. The advertisement pushing system according to claim 7, wherein the advertisement server is configured to determine, according to the target tag, an advertisement with the largest number of times of tagging the target tag.
10. The advertisement pushing system of claim 7, wherein the advertisement server is further configured to send the set of user parameters to the big data platform.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114445131A (en) * | 2022-01-20 | 2022-05-06 | 聚好看科技股份有限公司 | Startup advertisement delivery method, startup advertisement playing method, display device and server |
| WO2022226966A1 (en) * | 2021-04-30 | 2022-11-03 | 京东方科技集团股份有限公司 | Recommendation information sending method and positioning service device |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101655856A (en) * | 2009-09-15 | 2010-02-24 | 西安交通大学 | Method for obtaining user specific metaknowledge interest |
| CN102611785A (en) * | 2011-01-20 | 2012-07-25 | 北京邮电大学 | Personalized active news recommending service system and method for mobile phone user |
| CN102929928A (en) * | 2012-09-21 | 2013-02-13 | 北京格致璞科技有限公司 | Multidimensional-similarity-based personalized news recommendation method |
| CN102999589A (en) * | 2012-11-15 | 2013-03-27 | Tcl集团股份有限公司 | Method and system for recommending order |
| CN103164804A (en) * | 2011-12-16 | 2013-06-19 | 阿里巴巴集团控股有限公司 | Personalized method and personalized device of information push |
| CN103209342A (en) * | 2013-04-01 | 2013-07-17 | 电子科技大学 | Collaborative filtering recommendation method introducing video popularity and user interest change |
| CN103745382A (en) * | 2013-12-27 | 2014-04-23 | 中国联合网络通信有限公司广东省分公司 | Method, device and system of pushing an APP application group of advertising of smart city |
| CN103870512A (en) * | 2012-12-18 | 2014-06-18 | 腾讯科技（深圳）有限公司 | Method and device for generating user interest label |
| CN103995839A (en) * | 2014-04-30 | 2014-08-20 | 兴天通讯技术（天津）有限公司 | Commodity recommendation optimizing method and system based on collaborative filtering |
| CN104602042A (en) * | 2014-12-31 | 2015-05-06 | 合一网络技术(北京)有限公司 | User behavior based label setting method |
| CN106202475A (en) * | 2016-07-18 | 2016-12-07 | 合网络技术（北京）有限公司 | The method for pushing of a kind of video recommendations list and device |
| CN107016026A (en) * | 2016-11-11 | 2017-08-04 | 阿里巴巴集团控股有限公司 | A kind of user tag determination, information-pushing method and equipment |
| CN107730303A (en) * | 2017-09-15 | 2018-02-23 | 努比亚技术有限公司 | A kind of advertisement sending method, equipment and computer-readable recording medium |
| CN108052598A (en) * | 2017-12-12 | 2018-05-18 | 何旭连 | A kind of intelligent music commending system based on label |
| CN108776907A (en) * | 2018-05-31 | 2018-11-09 | 康键信息技术（深圳）有限公司 | Advertisement intelligent recommends method, server and storage medium |
| CN109034864A (en) * | 2018-06-11 | 2018-12-18 | 广东因特利信息科技股份有限公司 | Improve method, apparatus, electronic equipment and storage medium that precision is launched in advertisement |
| CN109657138A (en) * | 2018-12-10 | 2019-04-19 | 深圳墨世科技有限公司 | A kind of video recommendation method, device, electronic equipment and storage medium |
| CN109785017A (en) * | 2019-03-18 | 2019-05-21 | 康美药业股份有限公司 | Advertisement placement method, server and computer readable storage medium |
| CN110929166A (en) * | 2019-12-27 | 2020-03-27 | 咪咕文化科技有限公司 | Content recommendation method, electronic device and storage medium |
-
2020
- 2020-06-24 CN CN202010587792.2A patent/CN111738768A/en active Pending
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101655856A (en) * | 2009-09-15 | 2010-02-24 | 西安交通大学 | Method for obtaining user specific metaknowledge interest |
| CN102611785A (en) * | 2011-01-20 | 2012-07-25 | 北京邮电大学 | Personalized active news recommending service system and method for mobile phone user |
| CN103164804A (en) * | 2011-12-16 | 2013-06-19 | 阿里巴巴集团控股有限公司 | Personalized method and personalized device of information push |
| CN102929928A (en) * | 2012-09-21 | 2013-02-13 | 北京格致璞科技有限公司 | Multidimensional-similarity-based personalized news recommendation method |
| CN102999589A (en) * | 2012-11-15 | 2013-03-27 | Tcl集团股份有限公司 | Method and system for recommending order |
| CN103870512A (en) * | 2012-12-18 | 2014-06-18 | 腾讯科技（深圳）有限公司 | Method and device for generating user interest label |
| CN103209342A (en) * | 2013-04-01 | 2013-07-17 | 电子科技大学 | Collaborative filtering recommendation method introducing video popularity and user interest change |
| CN103745382A (en) * | 2013-12-27 | 2014-04-23 | 中国联合网络通信有限公司广东省分公司 | Method, device and system of pushing an APP application group of advertising of smart city |
| CN103995839A (en) * | 2014-04-30 | 2014-08-20 | 兴天通讯技术（天津）有限公司 | Commodity recommendation optimizing method and system based on collaborative filtering |
| CN104602042A (en) * | 2014-12-31 | 2015-05-06 | 合一网络技术(北京)有限公司 | User behavior based label setting method |
| CN106202475A (en) * | 2016-07-18 | 2016-12-07 | 合网络技术（北京）有限公司 | The method for pushing of a kind of video recommendations list and device |
| CN107016026A (en) * | 2016-11-11 | 2017-08-04 | 阿里巴巴集团控股有限公司 | A kind of user tag determination, information-pushing method and equipment |
| CN107730303A (en) * | 2017-09-15 | 2018-02-23 | 努比亚技术有限公司 | A kind of advertisement sending method, equipment and computer-readable recording medium |
| CN108052598A (en) * | 2017-12-12 | 2018-05-18 | 何旭连 | A kind of intelligent music commending system based on label |
| CN108776907A (en) * | 2018-05-31 | 2018-11-09 | 康键信息技术（深圳）有限公司 | Advertisement intelligent recommends method, server and storage medium |
| CN109034864A (en) * | 2018-06-11 | 2018-12-18 | 广东因特利信息科技股份有限公司 | Improve method, apparatus, electronic equipment and storage medium that precision is launched in advertisement |
| CN109657138A (en) * | 2018-12-10 | 2019-04-19 | 深圳墨世科技有限公司 | A kind of video recommendation method, device, electronic equipment and storage medium |
| CN109785017A (en) * | 2019-03-18 | 2019-05-21 | 康美药业股份有限公司 | Advertisement placement method, server and computer readable storage medium |
| CN110929166A (en) * | 2019-12-27 | 2020-03-27 | 咪咕文化科技有限公司 | Content recommendation method, electronic device and storage medium |
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