WO2020156236A1 - 关联信息的推送方法、装置，电子设备及存储介质 - Google Patents

关联信息的推送方法、装置，电子设备及存储介质 Download PDF

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Publication number
WO2020156236A1
WO2020156236A1 PCT/CN2020/072719 CN2020072719W WO2020156236A1 WO 2020156236 A1 WO2020156236 A1 WO 2020156236A1 CN 2020072719 W CN2020072719 W CN 2020072719W WO 2020156236 A1 WO2020156236 A1 WO 2020156236A1
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Prior art keywords
user
target
behavior data
target element
target article
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English (en)
French (fr)
Inventor
郑坤
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Beijing ByteDance Network Technology Co Ltd
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Beijing ByteDance Network Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/9536—Search customisation based on social or collaborative filtering

Definitions

  • the embodiments of the present disclosure relate to the field of computer technology, such as a method and device for pushing associated information, electronic equipment and storage media.
  • the embodiments of the present disclosure provide a method, a device, an electronic device, and a storage medium for pushing related information, which have avoided the situation that devices in related technologies have certain blindness when recommending information to users, and the recommended information is relatively random.
  • embodiments of the present disclosure provide a method for pushing associated information, including: obtaining user reading behavior data for a target article collected by a client; obtaining the association relationship between the target article and the target element from a storage module, The target element is associated with at least one target article; according to the user's reading behavior data for the target article and the association relationship between the target article and the target element, the relationship between the user and the target element is obtained Associated value; pushing the recommended information associated with the target element to the user according to the associated value of the user and the target element.
  • embodiments of the present disclosure provide a device for pushing associated information, including:
  • the first obtaining module is configured to obtain the user's reading behavior data for the target article collected by the client;
  • the second acquiring module is configured to acquire the association relationship between the target article and the target element from the storage module, and the target element is associated with at least one target article;
  • An association value determination module configured to obtain an association value between the user and the target element according to the user's reading behavior data for the target article and the association relationship between the target article and the target element;
  • the pushing module is configured to push the recommendation information associated with the target element to the user according to the associated value of the user and the target element.
  • an electronic device including:
  • At least one processor At least one processor
  • Memory set to store at least one program
  • At least one processor When at least one program is executed by at least one processor, at least one processor implements the method for pushing associated information as described in the first aspect of the embodiments of the present disclosure.
  • the embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the associated information as described in the first aspect of the embodiments of the present disclosure is realized.
  • the push method is realized.
  • FIG. 1 is a schematic flowchart of a method for pushing associated information provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of another method for pushing associated information provided by an embodiment of the present disclosure
  • FIG. 3 is a schematic flowchart of another method for pushing associated information provided by an embodiment of the present disclosure
  • FIG. 4 is a schematic structural diagram of a device for pushing associated information provided by an embodiment of the present disclosure
  • Fig. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • FIG. 1 shows a schematic flow chart of a method for pushing associated information provided by an embodiment of the present disclosure.
  • the method disclosed in an embodiment of the present disclosure is mainly suitable for pushing associated information to users based on their reading behavior or browsing habits.
  • the method can be executed by a corresponding push device of associated information, which can be implemented in software and/or hardware, and can be configured on an electronic device.
  • the method includes step S101 to step S104.
  • step S101 the user's reading behavior data for the target article collected by the client is obtained.
  • the target article refers to the article that the user is currently reading or the user has read, such as news information, biographies, or detailed information of a certain brand product that the user is reading or has read.
  • the reading behavior data includes data of click operations or non-click operations performed by the user on the target article.
  • the non-click operation data includes at least the stay time of the article title and the stay time of the article content.
  • the stay time of the article title refers to the length of time the user stays on the article title when the target article is not opened. Illustratively, it can be used through electronic devices.
  • the proactive and timer determination of the article content; the duration of the article content can be determined by counting the time from the user entering the target article to the user reading the end of the target article through the timer of the electronic device.
  • the click operation data includes at least one type of like behavior data, comment behavior data, or forwarding behavior data.
  • the reading behavior data includes at least one type of article title stay time, article content stay time, like behavior data, comment behavior data, and forwarding behavior data.
  • the user behavior log includes the user's reading behavior data for the target article
  • the user's reading behavior data for the target article collected by the client can be obtained from the user behavior log fed back by the client. Since the user behavior log is formed at a preset time interval, in order to ensure the efficiency of obtaining user reading behavior data, the user behavior log can be periodically obtained.
  • the user's information collected by the client for the target article The reading behavior data includes: periodically obtaining user behavior logs formed by the client at a preset time interval, and the user behavior logs include user reading behavior data for the target article.
  • user behavior logs are obtained once a day.
  • the obtained user's reading behavior data for the target article is stored in a preset format.
  • the specific reading behavior of any user for any target article is in accordance with the corresponding relationship of "user-article identification-specific reading behavior" Store it.
  • step S102 the association relationship between the target article and the target element is obtained from the storage module.
  • the target element includes at least one of an event, a person, and a brand
  • the target element is associated with at least one target article.
  • the relationship between the target article and the target element is determined in advance through server analysis or manual marking.
  • a special association server can be used to automatically analyze the article, extract and process keywords, and then match with the target element.
  • the keywords match the target element, establish an association relationship between the article and the target element, for example If an article is written as Jordan and contains the keyword Jordan, the relationship between the article and the target element Jordan is established; or the article’s creator or uploader can mark the article to mark which target elements it is associated with.
  • the pre-determined association relationship between the target article and the target element can be pre-stored in the storage module according to preset rules, for example, according to the corresponding relationship of "target article-brand".
  • the user when the user subsequently reads a certain target article, he can quickly find the association relationship between the target article and the target element from the storage module, that is, determine the target element corresponding to the target article.
  • step S103 the association value between the user and the target element is obtained according to the user's reading behavior data for the target article and the association relationship between the target article and the target element.
  • the associated value corresponding to the degree of association can be determined by the value corresponding to each type of reading behavior data, where each type of reading behavior data corresponds to
  • the value of is preset and exemplary, and the value corresponding to the reading behavior data is a preset empirical value used to characterize the degree of association between the user and the target article.
  • the user can perform multiple reading behaviors at the same time, so the value corresponding to all reading behavior data of the user for a target article (that is, the assignment of reading behavior data) can be determined to determine the associated value of the user and the target article .
  • the target element is associated with at least one target article, it is necessary to determine the number of target articles associated with a target element before calculating the associated value between the user and the target element.
  • the association value between the user and the target article is the association value between the user and the target element; when a target element is simultaneously associated with multiple target articles, the user is associated with The correlation value between each target article is summed, and the sum value is used as the correlation value between the user and the target element.
  • the target element M is respectively associated with target articles A, B, and C, and according to the above
  • the calculated correlation values between the user and the target article A, B, and C are 0.2, 0.3, and 0.35 in sequence.
  • the correlation value between the user and the target element M is equal to the sum of 0.2, 0.3, and 0.35, that is, the correlation value is 0.85.
  • step S104 the recommendation information associated with the target element is pushed to the user according to the associated value between the user and the target element.
  • a threshold value of the association value between the user and the target element can be set in advance.
  • the calculated association value is greater than the threshold value, it indicates that the user is interested in the content of the target element, so it can be targeted to the user Recommend the recommendation information corresponding to the target element.
  • the correlation value between the user and the target element for example, Huawei mobile phone P20
  • the correlation value threshold 0.6 the correlation value between the user and the target element
  • the user is recommended to the user related to the Huawei mobile phone P20, such as pushing various malls on the mobile phone promotion activities, Information such as the configuration information of the mobile phone or the user's experience of using the mobile phone.
  • the association value between the user and the target element is calculated, and recommended information related to the target element is pushed to the user.
  • the target element associated with the user is determined by acquiring the user's reading behavior, and only the information associated with the target element is pushed in a targeted manner, thereby improving the accuracy of the pushed information, thereby enhancing the user experience.
  • FIG. 2 shows another method for pushing associated information provided by an embodiment of the present disclosure.
  • This embodiment is refined on the basis of the foregoing embodiment, and the method includes steps S201 to S207.
  • step S201 the user's reading behavior data for the target article collected by the client is obtained.
  • step S202 the association relationship between the target article and the target element is obtained from the storage module, where the target element is associated with at least one target article.
  • step S203 the time interval in which the user's reading behavior data for the target article is located is acquired.
  • the time interval in which the reading behavior data is located includes at least two time intervals of different lengths, and according to the length of the time interval to which it belongs, the user reading behavior data is divided into short-term reading behavior data, mid-term reading behavior data, and long-term reading Behavioral data.
  • the time between the generation time of any reading behavior data and the current time can be determined according to the generation time and the current time of the user's reading behavior data for the target article, and the time between the generation time of the reading behavior data and the current time is less than 3
  • the reading behavior data is short-term reading behavior data; when the time between the generation of reading behavior data and the current time is greater than 3 days and less than 7 days, the reading behavior data is mid-term reading behavior data. If the time between the generation of the behavior data and the current time is greater than 7 days and less than 30 days, the reading behavior data is long-term reading behavior data.
  • step S204 the attenuation value corresponding to each time interval is obtained.
  • each time interval corresponds to an attenuation value
  • the time interval in which the reading behavior data is located is the difference between the current time and the time when the reading behavior data is generated, and the greater the difference, the greater the attenuation value corresponding to the time interval.
  • the attenuation value is 0; when the time period between the generation time of the reading behavior data and the current time is 3-7 days, the attenuation value is 20%; if the generation time of the reading behavior data is 7-30 days from the current time, the attenuation value is 40%.
  • step S205 according to the assignment of each type of reading behavior data of the user, the user's reading behavior data for the target article, the time interval in which the user's reading behavior data for the target article is located, and the corresponding attenuation value of each time interval, Get the association value between the user and the target article.
  • the correlation value between the user and the target article in each time period can be added, and the attenuation value can be subtracted to obtain the correlation value between the user and the target article.
  • step S206 the association value between the user and the target element is obtained according to the association value between the user and the target article, and the association relationship between the target article and the target element.
  • step S207 the recommendation information associated with the target element is pushed to the user according to the associated value between the user and the target element.
  • the reading behavior data includes at least one type of reading behavior data. According to the user's reading behavior data for the target article and the association relationship between the target article and the target element, obtaining the association value between the user and the target element includes:
  • the association value between the user and the target article and the association relationship between the target article and the target element is obtained.
  • the attenuation value is determined by determining the time interval to which the user reading behavior data belongs, thereby improving the accuracy of calculating the correlation value between the user and the target article according to the user reading behavior in each time period, so as to ensure the subsequent accurate calculation of the association between the user and the relevant element value.
  • FIG. 3 shows another method for pushing associated information provided by an embodiment of the present disclosure.
  • This embodiment is refined on the basis of the foregoing embodiment, and the method includes steps S301 to S304.
  • step S301 the user's reading behavior data for the target article collected by the client is obtained.
  • step S302 the association relationship between the target article and the target element is obtained from the storage module, where the target element is associated with at least one target article.
  • the association relationship between the target article and the target element is obtained from the associated value storage server; as another exemplary
  • the association value between the target article and the target element may also be stored in the local server in advance, so that the association relationship between the target article and the target element can be directly obtained from the storage module of the local server.
  • step S303 according to the user's reading behavior data for the target article and the association relationship between the target article and the target element, the association value between the user and the target element is obtained and stored in the association value storage server.
  • step S304 the recommendation server reads the correlation value between the user and the target element from the correlation value storage server, and pushes the recommendation information associated with the target element to the user according to the correlation value between the user and the target element.
  • the recommendation server directly reads the correlation value between the user and the target element from the correlation value server, and determines whether the correlation value is greater than a preset threshold, and when the correlation value is greater than the preset threshold, pushes to the user related to the target element Recommended information for the union.
  • the calculated correlation value between the user and the target element is stored in the correlation value storage server, so that the recommendation server directly reads the correlation value between the user and the target element from the correlation value storage server, thereby improving information push effectiveness.
  • the device for pushing associated information can be configured in an electronic device and includes a first acquiring module 401, a second acquiring module 402, and determining the associated value. Module 403 and push module 404.
  • the first obtaining module 401 is configured to obtain the user's reading behavior data for the target article collected by the client.
  • the second obtaining module 402 is configured to obtain the association relationship between the target article and the target element from the storage module, and the target element is associated with at least one target article.
  • the correlation value determination module 403 is configured to obtain the correlation value between the user and the target element according to the user's reading behavior data for the target article and the correlation relationship between the target article and the target element.
  • the pushing module 404 is configured to push the recommendation information associated with the target element to the user according to the associated value of the user and the target element.
  • the first acquisition module is configured to:
  • the reading behavior data includes at least one type of article title stay time, article content stay time, like behavior data, comment behavior data, and forwarding behavior data.
  • the correlation value determination module includes an acquisition and assignment unit, a first correlation value determination unit, and a second correlation value determination unit.
  • the obtaining assignment unit is set to obtain the assignment of each type of reading behavior data of the user.
  • the first correlation value determining unit is configured to obtain the correlation value between the user and the target article according to the assignment of each type of reading behavior data of the user and the reading behavior data of the user for the target article.
  • the second correlation value determining unit is configured to obtain the correlation value between the user and the target element according to the correlation value between the user and the target article and the correlation relationship between the target article and the target element.
  • the correlation value determination module further includes a time interval acquisition unit and an attenuation value acquisition unit.
  • the time interval acquiring unit is set to acquire the time interval in which the user's reading behavior data for the target article is located.
  • the attenuation value obtaining unit is set to obtain the attenuation value corresponding to each time interval.
  • the first correlation value determining unit is set to:
  • the user's reading behavior data for the target article According to the assignment of each type of reading behavior data of the user, the user's reading behavior data for the target article, the time interval in which the user's reading behavior data for the target article is located, and the attenuation value corresponding to each time interval, obtain The user's relevance value to the target article.
  • the time interval where the reading behavior data is located includes at least two time intervals of different lengths, and the time interval where the reading behavior data is located is the difference between the current time and the time when the reading behavior data is generated. , And the greater the difference, the greater the attenuation value corresponding to the time interval.
  • the target element includes at least one of an event, a person, and a brand.
  • the second acquisition module is configured to:
  • the device also includes:
  • the storage module is configured to store the associated value between the user and the target element in the associated value storage server.
  • the push module is set to:
  • the recommendation server reads the associated value of the user and the target element from the associated value storage server, and pushes the recommendation information associated with the target element to the user according to the associated value of the user and the target element.
  • the above associated information pushing device provided in the embodiment of the present disclosure can perform the steps performed by the electronic device in the method of pushing associated information provided in the method embodiment of the present disclosure.
  • FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. As shown in FIG. 5, it shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure.
  • the electronic device shown in FIG. 5 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.
  • the electronic device 500 may include a processor (such as a central processing unit, a graphics processor, etc.) 501, which may be based on a program stored in a read-only memory (Read-Only Memory, ROM) 502 or from a storage device 508
  • the program loaded into the random access memory (Random Access Memory, RAM) 503 executes various appropriate actions and processing, such as implementing the method for pushing associated information provided by the embodiments of the present disclosure, wherein the method for pushing associated information includes :
  • the RAM 503 also stores various programs and data required for the operation of the electronic device 500.
  • the processor 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504.
  • An input/output (I/O) interface 505 is also connected to the bus 504.
  • the following devices can be connected to the I/O interface 505: including input devices 506 such as touch screens, touch pads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; including, for example, liquid crystal displays (LCD) Output devices 507 such as speakers, vibrators, etc.; storage devices 508 such as tapes, hard disks, etc.; and communication devices 509.
  • the communication device 509 may allow the electronic device 500 to perform wireless or wired communication with other devices to exchange data.
  • FIG. 5 shows an electronic device 500 having various devices, it should be understood that it is not required to implement or have all the illustrated devices. It may alternatively be implemented or provided with more or fewer devices.
  • the process described above with reference to the flowchart can be implemented as a computer software program.
  • the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart.
  • the computer program may be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502.
  • the processor 501 When the computer program is executed by the processor 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.
  • the aforementioned computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above.
  • Computer-readable storage media may include, but are not limited to: electrical connection with at least one wire, portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable only Read memory (Erasable Programmable Read-Only Memory, EPROM or flash memory), optical fiber, portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical storage device, magnetic storage device, or any of the above suitable combination.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, and a computer-readable program code is carried therein.
  • This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
  • the computer-readable signal medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device .
  • the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wire, optical cable, radio frequency (RF), etc., or any suitable combination of the above.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist alone without being assembled into the electronic device.
  • the above-mentioned computer-readable medium carries at least one program, and when the above-mentioned at least one program is executed by the electronic device, the server is caused to execute the method for pushing related information provided in this embodiment, including: obtaining user information collected by the client for the target article Reading behavior data; obtain the association relationship between the target article and the target element from the storage module, and the target element is associated with at least one target article; obtain users according to the user's reading behavior data for the target article and the association relationship between the target article and the target element The value associated with the target element; according to the associated value between the user and the target element, the recommended information associated with the target element is pushed to the user.
  • the computer program code for performing the operations of the present disclosure may be written in at least one programming language or a combination thereof.
  • the above-mentioned programming language includes object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional procedural Programming language-such as "C" language or similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network-including Local Area Network (LAN) or Wide Area Network (WAN)-or it can be connected to an external computer (for example, use an Internet service provider to connect via the Internet).
  • LAN Local Area Network
  • WAN Wide Area Network
  • each block in the flowchart or block diagram may represent a module, program segment, or part of the code, and the module, program segment, or part of the code contains at least one option for realizing the specified logical function.
  • Execute instructions may also occur in a different order from the order marked in the drawings. For example, two blocks shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or operations Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the modules or units involved in the described embodiments of the present disclosure can be implemented in software or hardware.
  • the name of the module or unit does not constitute a limitation on the module itself under certain circumstances.
  • the push module can also be described as "pushing to the user the recommended information associated with the target element according to the associated value of the user and the target element.
  • the module ; the assignment acquisition unit can also be described as "a unit that acquires the assignment of each type of reading behavior data of the user".

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Abstract

本公开实施例公开了一种关联信息的推送方法、装置，电子设备及存储介质，其中，所述方法包括：获取客户端搜集的用户针对目标文章的阅读行为数据；从存储模块中获取所述目标文章与目标元素的关联关系，所述目标元素与至少一篇目标文章关联；根据所述用户针对所述目标文章的阅读行为数据，以及所述目标文章与所述目标元素的关联关系，获取所述用户与所述目标元素的关联值；根据所述用户与所述目标元素的关联值向用户推送与所述目标元素关联的推荐信息。

Description

关联信息的推送方法、装置，电子设备及存储介质
本申请要求在2019年1月29日提交中国专利局、申请号为201910088191.4的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本公开实施例涉及计算机技术领域，例如一种关联信息的推送方法、装置，电子设备及存储介质。
背景技术
随着互联网技术的飞速发展，人们在上网过程中会遇到各种各样纷繁复杂的信息，而且必须依靠主动搜索去查找自己想要的内容，例如，用户通过在网络的搜索引擎输入关键字来找到自己想要的书籍、电影、音乐、商品等。在智能化时代，这种繁琐的操作已经无法适应人们的需求，因此用户希望设备能够针对各自浏览情况主动的为其推送一些高质量的关联信息。然而目前，设备在向用户推荐信息时，具有一定的盲目性，推荐的信息比较随意，使得用户体验较差。
发明内容
本公开实施例提供一种关联信息的推送方法、装置，电子设备及存储介质，已避免相关技术中存在的设备在向用户推荐信息时，具有一定的盲目性，推荐的信息比较随意的情况。
第一方面，本公开实施例提供了一种关联信息的推送方法，包括：获取客户端搜集的用户针对目标文章的阅读行为数据；从存储模块中获取所述目标文章与目标元素的关联关系，所述目标元素与至少一篇目标文章关联；根据所述用户针对所述目标文章的阅读行为数据，以及所述目标文章与所述目标元素的关联关系，获取所述用户与所述目标元素的关联值；根据所述用户与所述目标元素的关联值向所述用户推送与所述目标元素关联的推荐信息。
第二方面，本公开实施例提供一种关联信息的推送装置，包括：
第一获取模块，设置为获取客户端搜集的用户针对目标文章的阅读行为数据；
第二获取模块，设置为从存储模块中获取所述目标文章与目标元素的关联关系，所述目标元素与至少一篇目标文章关联；
关联值确定模块，设置为根据所述用户针对目标文章的阅读行为数据，以及目标文章与所述目标元素的关联关系，获取用户与所述目标元素的关联值；
推送模块，设置为根据所述用户与所述目标元素的关联值向用户推送与所述目标元素关联的推荐信息。
第三方面，本公开实施例提供了一种电子设备，包括：
至少一个处理器；
存储器，设置为存储至少一个程序；
当至少一个程序被至少一个处理器执行，使得至少一个处理器实现如本公开实施例第一方面所述的关联信息的推送方法。
第四方面，本公开实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时，实现如本公开实施例第一方面所述的关联信息的推送方法。
附图说明
图1是本公开一实施例提供的一种关联信息的推送方法的流程示意图；
图2是本公开一实施例提供的另一种关联信息的推送方法的流程示意图；
图3是本公开实一施例提供的又一种关联信息的推送方法的流程示意图；
图4是本公开一实施例提供的一种关联信息的推送装置的结构示意图；
图5是本公开一实施例提供的一种电子设备的结构示意图。
具体实施方式
需要说明的是，本公开中术语“系统”和“网络”在本文中常被可互换使用。本公开实施例中提到的“和/或”是指”包括一个或更多个相关所列项目的任何和所有组合。本公开的说明书和权利要求书及附图中的术语“第一”、“第二”等是用于区别不同对象，而不是用于限定特定顺序。
还需要说明是，本公开下述各个实施例可以单独执行，各个实施例之间也可以相互结合执行，本公开实施例对此不作限制。
参考图1，其示出了本公开一实施例提供的一种关联信息的推送方法的流程 示意图，本公开实施例公开的方法主要适用于根据用户的阅读行为或浏览习惯为用户推送关联信息的情况，该方法可以由相应的关联信息的推送装置执行，该装置可以采用软件和/或硬件的方式实现，并可配置于电子设备上。
如图1所示，该方法包括步骤S101至步骤S104。
在步骤S101中，获取客户端搜集的用户针对目标文章的阅读行为数据。
其中，目标文章是指用户当前正在阅读或用户已经阅读过的文章，例如用户正阅读或已阅读过的新闻资讯、人物传记或某一品牌商品的详情信息等。而阅读行为数据包括用户针对该目标文章进行的点击操作数据或未点击操作数据。其中，未点击操作数据至少包括文章标题停留时长和文章内容停留时长，其中，文章标题停留时长是指用户未打开目标文章时，目光停留在文章标题上的时长，示例性的，可通过电子设备的前摄与计时器确定；文章内容停留时长可通过电子设备的计时器统计用户进入目标文章到用户阅读到目标文章的结尾所用时长来确定。点击操作数据至少包括点赞行为数据、评论行为数据或转发行为数据中的至少一种类型。在一实施例中，阅读行为数据包括文章标题停留时长、文章内容停留时长、点赞行为数据、评论行为数据、转发行为数据中的至少一种类型。
由于用户行为日志包括用户针对目标文章的阅读行为数据，因此可在客户端反馈的用户行为日志中获取客户端搜集的用户针对目标文章的阅读行为数据。由于用户行为日志是按照预设时间间隔形成的，因此，为保证获取用户阅读行为数据的效率，可周期性地获取用户行为日志，在一实施例中，获取客户端搜集的用户针对目标文章的阅读行为数据，包括：周期性地获取客户端按照预设时间间隔形成的用户行为日志，用户行为日志包括用户针对目标文章的阅读行为数据。示例性的，每天获取一次用户行为日志。并将获取的用户针对目标文章的阅读行为数据按照预设格式进行存储，示例性的，任一用户对任一目标文章的具体阅读行为，按照“用户-文章标识-具体阅读行为”的对应关系进行存储。
在步骤S102中，从存储模块中获取目标文章与目标元素的关联关系。
其中，目标元素包括事件、人物、品牌中的至少一项，且目标元素与至少一篇目标文章关联。而目标文章与目标元素之间的关联关系是预先通过服务器分析确定或人工标记确定的。示例性的，可利用专门的关联关系服务器自动对文章进行分析，提取处理关键字，然后跟目标元素进行匹配，在关键字与目标元素匹配的情况下，建立文章和目标元素的关联关系，例如一篇文章写得是乔 丹，里面含有关键字乔丹，则建立该篇文章与目标元素乔丹的关联关系；或者通过文章的创作者或上传者对该文章进行标记，标记其与哪些目标元素关联。针对预先已确定的目标文章与目标元素的关联关系，可按照预设规则预先存储在存储模块中，示例性的，按照“目标文章-品牌”的对应关系进行存储。
由此，后续在用户对某一目标文章产生阅读行为时，可从存储模块中快速查找出该目标文章与目标元素的关联关系，也即确定与该目标文章对应的目标元素。
在步骤S103中，根据用户针对目标文章的阅读行为数据，以及目标文章与目标元素的关联关系，获取用户与目标元素的关联值。
由于用户针对目标文章不同的阅读行为表征用户与该目标文章的关联程度，且关联程度对应的关联值可由通过每种类型的阅读行为数据对应的数值确定，其中，每种类型的阅读行为数据对应的数值是预先设定好的，示例性的，阅读行为数据对应的数值为预设的用于表征用户与目标文章关联程度的经验值。针对一篇目标文章，用户可同时进行多种阅读行为，因此可通过获取用户针对一篇目标文章的所有阅读行为数据对应的数值(即阅读行为数据的赋值)确定用户与该目标文章的关联值。例如，确定用户针对一篇目标文章所进行的所有阅读行为包括点赞行为、评论行为，而点赞行为和评论行为对应的数值分别为0.2、0.3，其中0.2、0.3可以为预先设定的经验值，则将点赞行为和评论行为对应的数值求和，并将和值0.5作为用户与所述目标文章的关联值。
由于目标元素与至少一篇目标文章关联，因此在计算用户与目标元素的关联值之前，需判断与一个目标元素相关联的目标文章的个数。在目标元素只与一个目标文章相关联的情况下，用户与目标文章的关联值即为用户与目标元素的关联值；在一个目标元素同时与多个目标文章相关联的情况下，将用户与每个目标文章之间的关联值进行求和运算，并将和值作为用户与该目标元素的关联值，示例性的，目标元素M分别与目标文章A、B、C相关联，而根据上述方法计算出的用户与目标文章A、B、C关联值依次为0.2、0.3、0.35，则用户与目标元素M的关联值等于0.2、0.3、0.35的和，即关联值为0.85。
在步骤S104中，根据所述用户与目标元素的关联值向用户推送与所述目标元素关联的推荐信息。
本实施例中，可预先设定一个用户与目标元素的关联值阈值，在计算出的关联值大于阈值的情况下，表明用户对目标元素相关内容比较感兴趣，因此可 有针对性的向用户推荐该目标元素对应的推荐信息。示例性的，通过计算用户与目标元素(例如华为手机P20)关联值为0.9，大于关联值阈值0.6，则向用户推荐与华为手机P20相关的信息，例如推送各个商城关于该手机的促销活动、该手机的配置信息或用户关于该手机的使用体验等信息。
本公开实施例中，根据用户对目标文章的阅读行为，以及目标文章与目标元素的关联关系，计算用户与目标元素的关联值，并向用户推送与目标元素相关的推荐信息。由此通过获取用户阅读行为确定与用户相关联的目标元素，有针对性的只推送与目标元素相关联的信息，以此提升的推送信息的准确性，进而提升用户体验。
参考图2，其示出了本公开一实施例提供的另一种关联信息的推送方法，本实施例是在上述实施例的基础上进行细化，该方法包括步骤S201至步骤S207。
在步骤S201中，获取客户端搜集的用户针对目标文章的阅读行为数据。
在步骤S202中，从存储模块中获取目标文章与目标元素的关联关系，其中，目标元素与至少一篇目标文章关联。
在步骤S203中，获取用户针对目标文章的阅读行为数据所处的时间区间。
其中，阅读行为数据所处的时间区间包括至少两种不同长度的时间区间，并根据所属的时间区间的长度的不同，将用户阅读行为数据分为短期阅读行为数据、中期阅读行为数据、长期阅读行为数据。示例性的，可根据用户针对目标文章的阅读行为数据的产生时间与当前时间，确定任一阅读行为数据的产生时间距当前时间的时长，在阅读行为数据的产生时间距当前时间的时长小于3天的情况下，该阅读行为数据为短期阅读行为数据；在阅读行为数据的产生时间距当前时间的时长大于3天且小于7天的情况下，该阅读行为数据为中期阅读行为数据，在阅读行为数据的产生时间距当前时间的时长大于7天且小于30天的情况下，该阅读行为数据为长期阅读行为数据。
在步骤S204中，获取每个时间区间对应的衰减值。
其中，每个时间区间都对应一个衰减值，阅读行为数据所处的时间区间为当前时间与阅读行为数据产生的时间的差值，且差值越大，该时间区间对应的衰减值越大。示例性的，在阅读行为数据的产生时间距当前时间的时长小于3天的情况下，衰减值为0；在阅读行为数据的产生时间距当前时间的时长在3-7天的，衰减值为20％；阅读行为数据的产生时间距当前时间的时长在7-30天的， 衰减值为40％。
在步骤S205中，根据用户每种类型的阅读行为数据的赋值、用户针对目标文章的阅读行为数据、用户针对目标文章的阅读行为数据所处的时间区间和每个时间区间的对应的衰减值，获取用户与目标文章的关联值。
其中，可将各个时间段的用户与目标文章的关联值相加，并减去衰减值即为用户与目标文章的关联值。示例性的，针对目标文章A，通过上述实施例提供的计算用户和目标文章的关联值的方法，在时间区间0-3天、3-7天、7-30天内用户与目标文章的关联值依次为0.4、0.3、0.4，则在30天中用户与该目标文章的关联度为0.4*(1-0)+0.3*(1-20％)+0.4*(1-40％)＝0.88。
在步骤S206中，根据用户与所述目标文章的关联值，以及目标文章和目标元素的关联关系，获取用户与目标元素的关联值。
在步骤S207中，根据用户与目标元素的关联值向用户推送与目标元素关联的推荐信息。
在一实施例中，阅读行为数据包括至少一种类型的阅读行为数据，根据用户针对目标文章的阅读行为数据，以及目标文章与目标元素的关联关系，获取用户与目标元素的关联值，包括：
获取用户每种类型的阅读行为数据的赋值；
根据用户每种类型的阅读行为数据的赋值，以及用户针对目标文章的阅读行为数据，获取用户与目标文章的关联值；
根据用户与目标文章的关联值，以及目标文章和目标元素的关联关系，获取用户与目标元素的关联值。
本实施例通过确定用户阅读行为数据所属的时间区间，确定衰减值，进而提高根据各个时间段的用户阅读行为计算用户与目标文章关联值的准确性，以保证后续准确计算用户与相关元素的关联值。
参考图3，其示出了本公开一实施例提供的又一种关联信息的推送方法，本实施例是在上述实施例的基础上进行细化，该方法包括步骤S301至步骤S304。
在步骤S301中，获取客户端搜集的用户针对目标文章的阅读行为数据。
在步骤S302中，从存储模块中获取目标文章与目标元素的关联关系，其中，目标元素与至少一篇目标文章关联。
示例性的，在目标文章与目标元素的关联值被预先存储到关联值存储服务 器的情况下，从关联值存储服务器中获取所述目标文章与目标元素的关联关系；作为另一种示例性的实施方式，也可以预先将目标文章与目标元素的关联值存储到本地服务器，以便后续直接从本地服务器的存储模块中获取目标文章与目标元素的关联关系。
在步骤S303中，根据用户针对目标文章的阅读行为数据，以及目标文章与所述目标元素的关联关系，获取用户与目标元素的关联值，并存储到关联值存储服务器中。
在步骤S304中，推荐服务器从关联值存储服务器中读取用户与目标元素的关联值，并根据用户与目标元素的关联值向用户推送与目标元素关联的推荐信息。
其中，推荐服务器直接从关联值服务器中读取用户与目标元素的关联值，并判断该关联值是否大于预设阈值，在该关联值大于预设阈值的情况下，向用户推送与目标元素相关联的推荐信息。
本实施例中，对于计算好的用户与目标元素的关联值存储在关联值存储服务器中，以便推荐服务器直接从关联值存储服务器中读取用户与目标元素的关联值，由此可提升信息推送效率。
图4为本公开一实施例提供的一种关联信息的推送装置的结构示意图，该关联信息的推送装置可以配置于电子设备中，包括第一获取模块401、第二获取模块402、关联值确定模块403以及推送模块404。
第一获取模块401，设置为获取客户端搜集的用户针对目标文章的阅读行为数据。
第二获取模块402，设置为从存储模块中获取所述目标文章与目标元素的关联关系，所述目标元素与至少一篇目标文章关联。
关联值确定模块403，设置为根据所述用户针对目标文章的阅读行为数据，以及目标文章与所述目标元素的关联关系，获取用户与所述目标元素的关联值。
推送模块404，设置为根据所述用户与目标元素的关联值向用户推送与所述目标元素关联的推荐信息。
在一实施例中，所述第一获取模块设置为：
周期性地获取客户端按照预设时间间隔形成的用户行为日志，所述用户行为日志包括所述用户针对目标文章的阅读行为数据。
在一实施例中，所述阅读行为数据包括文章标题停留时长、文章内容停留时长、点赞行为数据、评论行为数据、转发行为数据中的至少一种类型。
在一实施例中，所述关联值确定模块包括获取赋值单元、第一关联值确定单元以及第二关联值确定单元。
获取赋值单元，设置为获取用户每种类型的阅读行为数据的赋值。
第一关联值确定单元，设置为根据用户每种类型的阅读行为数据的赋值，以及所述用户针对目标文章的阅读行为数据，获取用户与所述目标文章的关联值。
第二关联值确定单元，设置为根据用户与所述目标文章的关联值，以及目标文章和目标元素的关联关系，获取用户与所述目标元素的关联值。
在一实施例中，关联值确定模块还包括时间区间获取单元以及衰减值获取单元。
时间区间获取单元，设置为获取用户针对目标文章的阅读行为数据所处的时间区间。
衰减值获取单元，设置为获取每个时间区间对应的衰减值。
所述第一关联值确定单元设置为：
根据用户每种类型的阅读行为数据的赋值、所述用户针对目标文章的阅读行为数据、所述用户针对目标文章的阅读行为数据所处的时间区间和每个时间区间对应的的衰减值，获取用户对所述目标文章的关联值。
在一实施例中所述阅读行为数据所处的时间区间包括至少两种不同长度的时间区间，所述阅读行为数据所处的时间区间为当前时间与所述阅读行为数据产生的时间的差值，且所述差值越大，所述时间区间对应的衰减值越大。
在一实施例中，所述目标元素包括事件、人物、品牌中的至少一项。
在一实施例中，所述第二获取模块设置为：
从关联值存储服务器中获取所述目标文章与目标元素的关联关系，或者，从本地服务器的存储模块中获取所述目标文章与目标元素的关联关系。
所述装置还包括：
存储模块，设置为将所述用户与所述目标元素的关联值存储到关联值存储服务器中。
所述推送模块设置为：
推荐服务器从所述关联值存储服务器中读取所述用户与所述目标元素的关 联值，并根据所述用户与所述目标元素的关联值向用户推送与所述目标元素关联的推荐信息。
本公开实施例提供的以上关联信息的推送装置，可执行本公开方法实施例所提供的关联信息的推送方法中电子设备所执行的步骤。
图5为本公开一实施例提供的一种电子设备的结构示意图，如图5所示，其示出了适于用来实现本公开实施例的电子设备的结构示意图。图5示出的电子设备仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图5所示，电子设备500可以包括处理器(例如中央处理器、图形处理器等)501，其可以根据存储在只读存储器(Read-Only Memory，ROM)502中的程序或者从存储装置508加载到随机访问存储器(Random Access Memory，RAM)503中的程序而执行各种适当的动作和处理，例如实现本公开实施例所提供的关联信息的推送方法，其中该关联信息的推送方法包括：
获取客户端搜集的用户针对目标文章的阅读行为数据；
从存储模块中获取目标文章与目标元素的关联关系，目标元素与至少一篇目标文章关联；
根据用户针对目标文章的阅读行为数据，以及目标文章与目标元素的关联关系，获取用户与目标元素的关联值；
根据用户与目标元素的关联值向用户推送与目标元素关联的推荐信息。
在RAM 503中，还存储有电子设备500操作所需的各种程序和数据。处理器501、ROM 502以及RAM 503通过总线504彼此相连。输入/输出(Input/Output，I/O)接口505也连接至总线504。
通常，以下装置可以连接至I/O接口505：包括例如触摸屏、触摸板、键盘、鼠标、摄像头、麦克风、加速度计、陀螺仪等的输入装置506；包括例如液晶显示器(Liquid Crystal Display，LCD)、扬声器、振动器等的输出装置507；包括例如磁带、硬盘等的存储装置508；以及通信装置509。通信装置509可以允许电子设备500与其他设备进行无线或有线通信以交换数据。虽然图5示出了具有各种装置的电子设备500，但是应理解的是，并不要求实施或具备所有示出的装置。可以替代地实施或具备更多或更少的装置。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为 计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置509从网络上被下载和安装，或者从存储装置508被安装，或者从ROM 502被安装。在该计算机程序被处理器501执行时，执行本公开实施例的方法中限定的上述功能。
需要说明的是，本公开上述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有至少一个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(Compact Disc Read-Only Memory，CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读信号介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
上述计算机可读介质可以是上述电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。
上述计算机可读介质承载有至少一个程序，当上述至少一个程序被该电子设备执行时，使得该服务器执行本实施例提供的关联信息的推送方法，包括：获取客户端搜集的用户针对目标文章的阅读行为数据；从存储模块中获取目标文章与目标元素的关联关系，目标元素与至少一篇目标文章关联；根据用户针对目标文章的阅读行为数据，以及目标文章与目标元素的关联关系，获取用户 与目标元素的关联值；根据用户与目标元素的关联值向用户推送与目标元素关联的推荐信息。
可以以至少一种程序设计语言或其组合来编写用于执行本公开的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(Local Area Network，LAN)或广域网(Wide Area Network，WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含至少一个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的模块或单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，模块或单元的名称在某种情况下并不构成对该模块本身的限定，例如，推送模块还可以被描述为“根据用户与目标元素的关联值向用户推送与目标元素关联的推荐信息的模块”；赋值获取单元还可以被描述为“获取用户每种类型的阅读行为数据的赋值的单元”。

Claims (11)

  1. 一种关联信息的推送方法，包括：
    获取客户端搜集的用户针对目标文章的阅读行为数据；
    从存储模块中获取所述目标文章与目标元素的关联关系，所述目标元素与至少一篇目标文章关联；
    根据所述用户针对所述目标文章的阅读行为数据，以及所述目标文章与所述目标元素的关联关系，获取所述用户与所述目标元素的关联值；
    根据所述用户与所述目标元素的关联值向所述用户推送与所述目标元素关联的推荐信息。
  2. 根据权利要求1所述的方法，其中，所述获取客户端搜集的用户针对目标文章的阅读行为数据，包括：
    周期性地获取客户端按照预设时间间隔形成的用户行为日志，所述用户行为日志包括所述用户针对所述目标文章的阅读行为数据。
  3. 根据权利要求1所述的方法，其中，所述阅读行为数据包括文章标题停留时长、文章内容停留时长、点赞行为数据、评论行为数据、转发行为数据中的至少一种类型。
  4. 根据权利要求1所述的方法，其中，所述阅读行为数据包括至少一种类型的阅读行为数据，所述根据所述用户针对所述目标文章的阅读行为数据，以及所述目标文章与所述目标元素的关联关系，获取所述用户与所述目标元素的关联值，包括：
    获取所述用户每种类型的阅读行为数据的赋值；
    根据所述用户每种类型的阅读行为数据的赋值，以及所述用户针对所述目标文章的阅读行为数据，获取所述用户与所述目标文章的关联值；
    根据所述用户与所述目标文章的关联值，以及所述目标文章和所述目标元素的关联关系，获取所述用户与所述目标元素的关联值。
  5. 根据权利要求4所述的方法，还包括：
    获取所述用户针对所述目标文章的阅读行为数据所处的时间区间；
    获取每个时间区间对应的衰减值；
    所述根据用户每种类型的阅读行为数据的赋值，以及所述用户针对所述目标文章的阅读行为数据，获取用户对所述目标文章的关联值，包括：
    根据所述用户每种类型的阅读行为数据的赋值、所述用户针对所述目标文章的阅读行为数据、所述用户针对所述目标文章的阅读行为数据所处的时间区 间和每个时间区间对应的衰减值，获取所述用户对所述目标文章的关联值。
  6. 根据权利要求5所述的方法，其中，所述阅读行为数据所处的时间区间包括至少两种具有不同长度的时间区间，所述阅读行为数据所处的时间区间为当前时间与所述阅读行为数据产生的时间的差值，且所述差值越大，所述时间区间对应的衰减值越大。
  7. 根据权利要求1所述的方法，其中，所述目标元素包括事件、人物、品牌中的至少一项。
  8. 根据权利要求1所述的方法，其中，所述从存储模块中获取所述目标文章与目标元素的关联关系，包括：
    从关联值存储服务器中获取所述目标文章与所述目标元素的关联关系，或者，从本地服务器的存储模块中获取所述目标文章与所述目标元素的关联关系；
    所述方法，还包括：
    将所述用户与所述目标元素的关联值存储到关联值存储服务器中；
    所述根据用户与所述目标元素的关联值向用户推送与所述目标元素关联的推荐信息，包括：
    推荐服务器从所述关联值存储服务器中读取所述用户与所述目标元素的关联值，并根据所述用户与所述目标元素的关联值向用户推送与所述目标元素关联的推荐信息。
  9. 一种关联信息的推送装置，包括：
    第一获取模块，设置为获取客户端搜集的用户针对目标文章的阅读行为数据；
    第二获取模块，设置为从存储模块中获取所述目标文章与目标元素的关联关系，所述目标元素与至少一篇目标文章关联；
    关联值确定模块，设置为根据所述用户针对所述目标文章的阅读行为数据，以及所述目标文章与所述目标元素的关联关系，获取所述用户与所述目标元素的关联值；
    推送模块，设置为根据所述用户与所述目标元素的关联值向所述用户推送与所述目标元素关联的推荐信息。
  10. 一种电子设备，包括：
    至少一个处理器；
    存储器，设置为存储至少一个程序；
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-8中任一项所述的关联信息的推送方法。
  11. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时，实现如权利要求1-8中任一项所述的关联信息的推送方法。
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