CN103455485A - Method and device for automatically updating user interest model - Google Patents
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Abstract
The invention relates to a method and a device for automatically updating a user interest model. The method particularly includes the steps: filtering push messages according to a current user interest model, and displaying the push messages to a user; acquiring feedback data after the user browses the push messages, and automatically updating the user interest model after analyzing the feedback data. According to the method and the device for automatically updating the user interest model, messages not interesting the user are filtered by the user interest model, the user interest model is automatically updated according to feedback of the user, so that better personalized service is provided for the user, and search efficiency is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for automatically updating a user interest model.
Background
At present, people often obtain network information by searching information after inputting keywords by a search engine. However, such a keyword query-based method has a low hit rate, and it is difficult to satisfy query requests for different purposes, different backgrounds, and different periods, and cannot automatically adjust to cater to the browsing interest of each user and automatically recommend a page to the user in real time, thereby failing to quickly provide the user with the service he or she needs. On the other hand, with the birth of the push information, the user often receives unnecessary junk push information, and the message push without distinguishing the user interest seriously interferes the life of the user, so that the merchant providing the push service cannot achieve the purpose of using the promotion service.
The personalized service technology can effectively solve the problems, the core of the technology is to match the requirements of users, and therefore, how to solve the establishment and updating technology of user interest models becomes the key of personalized services.
Disclosure of Invention
The invention mainly aims to provide a method and a device for automatically updating a user interest model so as to provide personalized services which are in line with the interests of users.
The invention provides a method for automatically updating a user interest model, which comprises the following steps:
filtering the push message according to the current user interest model and displaying the push message to the user;
and acquiring feedback data after the user browses the push message, and automatically updating the user interest model after analyzing the feedback data.
Preferably, the step of filtering the push message according to the current user interest model and displaying the push message to the user further comprises:
receiving interest keywords input by a user and the weight of each interest keyword;
and expressing the user interest as a vector by using a vector space model method, and establishing an initial interest model of the user.
Preferably, the feedback of the user comprises positive feedback and negative feedback, the positive feedback is when the feedback of the user is interested in the push message, and the negative feedback is when the feedback of the user is not interested in the push message.
Preferably, the step of automatically updating the user interest model after analyzing the feedback data specifically includes:
when the feedback of the user is the positive feedback, adopting a formula
Updating the user interest model;
when the feedback of the user is the negative feedback, a formula is adoptedUpdating the user interest model;
wherein,
for the new user interest vector to be used,
for the old user interest vector, the user is,for the vector representation of the feedback document, α is the old user interest vector weight, β is the positive feedback weight, γ is the negative feedback weight, and α + β is 1, α + γ is 1.
Preferably, the step of automatically updating the user interest model after analyzing the feedback of the user further comprises:
and when the user is offline, updating the user interest model by using all the obtained feedback and a preset formula.
The invention also provides a device for automatically updating the user interest model, which comprises:
the filtering module is used for filtering the push message according to the current user interest model and displaying the push message to the user;
and the updating module is used for receiving feedback of the user after browsing the push message and automatically updating the user interest model according to the feedback of the user.
Preferably, the apparatus further comprises:
the input module is used for receiving interest keywords input by a user and the weight of each interest keyword;
and the building module is used for representing the user interest as a vector by using a vector space model method and building an initial user interest model.
Preferably, the feedback of the user comprises positive feedback and negative feedback, the positive feedback is when the feedback of the user is interested in the push message, and the negative feedback is when the feedback of the user is not interested in the push message.
Preferably, the update module is specifically configured to:
when the feedback of the user is the positive feedback, adopting a formula
Updating the user interest model;
when the feedback of the user is the negative feedback, a formula is adopted
Updating the user interest model;
wherein,
for the new user interest vector to be used,for old useThe vector of interest of the user is,for the vector representation of the feedback document, α is the old user interest vector weight, β is the positive feedback weight, γ is the negative feedback weight, and α + β is 1, α + γ is 1.
Preferably, the apparatus further comprises an offline updating module, configured to:
and when the user is offline, updating the user interest model by using all the obtained feedback and a preset formula.
According to the method and the device for automatically updating the user interest model, the information which is not interested by the user is filtered through the user interest model, and the user interest model is automatically updated according to the feedback of the user, so that better personalized service is provided for the user, and the searching efficiency is improved.
Drawings
FIG. 1 is a flow chart illustrating an embodiment of automatically updating a user interest model according to the present invention;
FIG. 2 is a flow chart illustrating another embodiment of automatically updating a user interest model according to the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of an apparatus for automatically updating a user interest model according to the present invention;
FIG. 4 is a schematic structural diagram of another embodiment of an apparatus for automatically updating a user interest model according to the present invention;
fig. 5 is a schematic structural diagram of a further embodiment of the apparatus for automatically updating a user interest model according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The application range of the method and the device for automatically updating the user model comprises personalized services such as push message filtering, commodity recommendation of various electronic commerce websites, forum service recommendation, RSS subscription content filtering and the like. The drift of the user interest is tracked by adopting a preset formula, and a method for updating the user interest model in a self-adaptive manner is provided, so that personalized service can be better provided for the user, and the requirements of the user are met to the greatest extent. In the embodiments provided in the present invention, the push message filtering is taken as an example for further description, and those skilled in the art can know that the implementation manner in other similar personalized services is similar to this, and will not be described herein again.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for automatically updating a user interest model according to an embodiment of the present invention, as shown in fig. 1, the method specifically includes the following steps:
s10: filtering the push message according to the current user interest model and displaying the push message to the user;
according to the current user interest model, the system filters the push messages, and simultaneously stores the push messages irrelevant to the current user interest for the convenience of the user to check later. And after the filtering is finished, the system displays the filtered push message to the user. In other embodiments, the system may also delete push messages that are not relevant to the current user interest to conserve system memory.
S20: acquiring feedback data after a user browses the push message, and automatically updating a user interest model after analyzing the feedback data;
in this embodiment, the system displays the push message to the user and simultaneously prompts the user to select whether the push message is interested, for example, pops up a question "whether the push message is interested" to the user and accepts the selection of the user, or prompts the question to the user at the end of the push message and accepts the selection of the user, and automatically updates the user interest model according to the feedback data of the user.
Further, feedback information of the user is divided into positive feedback and negative feedback, the positive feedback is used when the feedback of the user is interested in the push information, and the negative feedback is used when the feedback of the user is not interested in the push information, so that the positive feedback and the negative feedback are distinguished to update the user interest model in a more targeted manner. In other embodiments, the system may also count keywords of the push message that the user has read, analyze the interest of the user according to the counted keywords, and automatically update the user interest model according to the obtained data. After the user interest model is updated according to the feedback data of the user, when new push information enters the system, the interest of the user can be tracked, and part of push information is filtered, so that more personalized service is provided for the user.
More specifically, in this embodiment, when the user reads a certain push message and feeds back the evaluation of the push message, and when the user is interested in the message, the evaluation is single positive feedback, according to the rocchi single positive feedback algorithm formula
Updating the user interest model; when the user is not interested in the message, the message is subjected to single negative feedback according to a Rocchio single negative feedback algorithm formula
And updating the user interest model. Wherein,
for the new user interest vector to be used,
for the old user interest vector, the user is,
for the vector representation of the feedback document, α is the old user interest vector weight, β is the positive feedback weight, γ is the negative feedback weight, and α + β is 1, α + γ is 1.
After the updating of the user interest model is completed, whether the user exits the system or not is judged, if yes, the online updating is finished, and otherwise, the updating of the user interest model is continued.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for automatically updating a user interest model according to another embodiment of the present invention, as shown in fig. 2, before step S10, the method further includes:
step S30: receiving interest keywords input by a user and the weight of each interest keyword;
when a user logs in for the first time, the user interest model is not established yet, and because no record is accessed, the user needs to input the interest keywords and the weight of each interest keyword, and the system receives the information input by the user so as to establish the initialized user interest model by the system. In other embodiments, a user may also register when logging in the system for the first time, and when registering, basic information (including personal information such as a user name and a password) is first input, so that the user is associated with a corresponding user interest model, and a plurality of user interest models are established in the same system.
Step S40: expressing the user interest as a vector by using a vector control model method, and establishing an initial interest model of the user;
more specifically, the user interest model is represented by an interest keyword vector, namely, the user interest is represented as a set { word) composed of interest keywords1,word2，...,wordnWherein wordj(j =1, 2.. and n) represents an interest item j of the user. Each timeAn interest item wordjEndowing a certain weight omega according to the level of user interestjAnd is and
the user interest model can be represented as a set of 1 binary, Profile = { word = } words1，ω1)，(word2，ω2)，...，(wordn，ωn) I.e. the initial user interest model vector. After the initial user interest model is established, the user interest model is updated according to feedback of reading push information of a subsequent user, so that the user interest model is more suitable for the personalized requirements of the user.
On the basis of the foregoing embodiment, in another embodiment, the step of automatically updating the user interest model after analyzing the feedback of the user further includes:
when the user is off-line, all the obtained feedback is used for distinguishing positive feedback from negative feedback and then a formula is used Updating the user interest model;
when the user quits the system, the system also updates the user interest model in an off-line manner by using a Rocchio algorithm according to the acquired multiple feedback data, and the specific steps comprise:
collecting all feedback information of the user when the user is online;
distinguishing positive feedback information and negative feedback information from all the collected feedback information;
according to And updating the corresponding user interest model.
Through a multi-feedback algorithm used in an offline state, the deviation generated when single feedback is used in an online state can be corrected, the stability of the whole system is improved, and the user interest model is closer to the interest of the user.
According to the method for automatically updating the user interest model, the information which is not interested by the user is filtered through the user interest model, and the user interest model is automatically updated according to the feedback of the user, so that better personalized service is provided for the user, and the searching efficiency is improved.
Referring to fig. 3, another embodiment of the present invention further provides an apparatus for automatically updating a user interest model, as shown in fig. 3, the apparatus specifically includes:
a filtering module 100, configured to filter the push message according to a current user interest model and display the push message to a user;
and the updating module 200 is configured to receive feedback of the user after browsing the push message, and automatically update the user interest model according to the feedback of the user.
According to the current user interest model, the filtering module 100 filters the push messages, and additionally stores the push messages irrelevant to the current user interest for the user to view later. After filtering, the filtering module 100 displays the filtered push message to the user. In other embodiments, the filtering module 100 may also delete push messages that are not relevant to the current user interest to conserve system memory.
In this embodiment, the update module 200 displays the push message to the user and simultaneously prompts the user to select whether the push message is interested, for example, the update module 200 pops up a question "whether the push message is interested" to the user and accepts the selection of the user, or prompts the question to the user at the end of the push message and accepts the selection of the user, and automatically updates the user interest model according to the feedback data of the user. Further, feedback information of the user is divided into positive feedback and negative feedback, the positive feedback is used when the feedback of the user is interested in the push information, and the negative feedback is used when the feedback of the user is not interested in the push information, so that the positive feedback and the negative feedback are distinguished to update the user interest model in a more targeted manner. In other embodiments, the updating module 200 may also count keywords of the push message that has been read by the user, analyze the interest of the user according to the counted keywords, and automatically update the user interest model according to the obtained data. After the updating module 200 updates the user interest model according to the feedback data of the user, when a new push message enters the system, the user interest can be tracked, and part of the push message is filtered, so that more personalized service is provided for the user.
More specifically, in this embodiment, when the user reads a certain push message and feeds back the evaluation of the push message, and when the user is interested in the message, the evaluation is single positive feedback, according to the rocchi single positive feedback algorithm formula
Updating the user interest model; when the user is not interested in the message, the message is subjected to single negative feedback according to a Rocchio single negative feedback algorithm formula
And updating the user interest model. Wherein,for the new user interest vector to be used,
for the old user interest vector, the user is,
for the vector representation of the feedback document, α is the old user interest vector weight, β is the positive feedback weight, γ is the negative feedback weight, and α + β is 1, α + γ is 1.
After the updating of the user interest model is completed, whether the user exits the system or not is judged, if yes, the online updating is finished, and otherwise, the updating of the user interest model is continued.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an apparatus for automatically updating a user interest model according to another embodiment of the present invention, as shown in fig. 4, the apparatus further includes:
an input module 300 for accepting an interest keyword and a weight of each interest keyword input by a user;
the building module 400 is configured to represent the user interest as a vector by using a vector space model method, and build a user interest model.
When the user logs in for the first time, the user interest model is not established yet, and since no record is accessed, the user needs to input the interest keywords and the weight of each interest keyword, and the building module 400 receives the information input by the user so as to facilitate the system to establish the initialized user interest model. In other embodiments, a user may also register when logging in the system for the first time, and when registering, basic information (including personal information such as a user name and a password) is first input, so that the user is associated with a corresponding user interest model, and a plurality of user interest models are established in the same system.
More specifically, the building module 400 represents the user interest model using the interest keyword vector, i.e. the user interest is represented as a set of interest keywords { word }1,word2,…,wordnWherein wordj(j =1, 2.. and n) represents an interest item j of the user. Each interest item wordjEndowing a certain weight omega according to the level of user interestjAnd is and
the user interest model can be represented as a set of 1 bigrams, Profile = { (word)1，ω1)，(word2，ω2)，...，(wordn，ωn) I.e. the initial user interest model vector. After the initial user interest model is established, the user interest model is updated according to feedback of reading push information of a subsequent user, so that the user interest model is more suitable for the personalized requirements of the user.
On the basis of the foregoing embodiment, referring to fig. 5, in another embodiment, the apparatus for automatically updating a user interest model further includes an offline updating module 500, configured to use all obtained feedback to distinguish between positive feedback and negative feedback and then use a formula when the user is offline Updating the user interest model;
after the user exits the system, the apparatus further updates the user interest model offline according to the obtained multiple feedback data, and the specific steps of the offline updating module 500 updating the user interest model include:
collecting all feedback information of the user when the user is online;
distinguishing positive feedback information and negative feedback information from all the collected feedback information;
according to And updating the corresponding user interest model.
Through a multi-feedback algorithm used in an offline state, the deviation generated when single feedback is used in an online state can be corrected, the stability of the whole system is improved, and the user interest model is closer to the interest of the user.
According to the device for automatically updating the user interest model, the information which is not interesting to the user is filtered through the user interest model, and the user interest model is automatically updated according to the feedback of the user, so that better personalized service is provided for the user, and the searching efficiency is improved.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for automatically updating a user interest model, comprising:
filtering the push message according to the current user interest model and displaying the push message to the user;
and acquiring feedback data after the user browses the push message, and automatically updating the user interest model after analyzing the feedback data.
2. The method of claim 1, wherein the step of filtering the push message according to the current user interest model and displaying the push message to the user is preceded by the step of:
receiving interest keywords input by a user and the weight of each interest keyword;
and expressing the user interest as a vector by using a vector space model method, and establishing an initial interest model of the user.
3. The method of claim 1, wherein the feedback of the user comprises positive feedback and negative feedback, wherein the positive feedback is when the user feedback is interested in the push message, and wherein the negative feedback is when the user feedback is not interested in the push message.
4. The method according to claim 1, wherein the step of automatically updating the user interest model after analyzing the feedback data specifically comprises:
when the feedback of the user is the positive feedback, adopting a formula
Updating the user interest model;
when the feedback of the user is the negative feedback, a formula is adopted
Updating the user interest model;
wherein,
for the new user interest vector to be used,for the old user interest vector, the user is,
for the vector representation of the feedback document, α is the old user interest vector weight, β is the positive feedback weight, γ is the negative feedback weight, and α + β is 1, α + γ is 1.
5. The method of any of claims 1 to 4, wherein the step of automatically updating the user interest model after analyzing the user's feedback data further comprises:
and when the user is offline, updating the user interest model by using all the obtained feedback and a preset formula.
6. An apparatus for automatically updating a user interest model, comprising:
the filtering module is used for filtering the push message according to the current user interest model and displaying the push message to the user;
and the updating module is used for receiving feedback of the user after browsing the push message and automatically updating the user interest model according to the feedback of the user.
7. The apparatus of claim 6, further comprising:
the input module is used for receiving interest keywords input by a user and the weight of each interest keyword;
and the building module is used for representing the user interest as a vector by using a vector space model method and building an initial user interest model.
8. The apparatus of claim 6, wherein the user feedback comprises positive feedback and negative feedback, wherein the positive feedback is when the user feedback is interested in the push message, and wherein the negative feedback is when the user feedback is not interested in the push message.
9. The apparatus of claim 6, wherein the update module is specifically configured to:
when the feedback of the user is the positive feedback, adopting a formula
Updating the user interest model;
when the feedback of the user is the negative feedback, a formula is adoptedUpdating the user interest model;
wherein,
for the new user interest vector to be used,
for the old user interest vector, the user is,
for the vector representation of the feedback document, α is the old user interest vector weight, β is the positive feedback weight, γ is the negative feedback weight, and α + β is 1, α + γ is 1.
10. The apparatus of any one of claims 6 to 9, further comprising an offline update module to:
and when the user is offline, updating the user interest model by using all the obtained feedback and a preset formula.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933163A (en) * | 2015-06-26 | 2015-09-23 | 广西中烟工业有限责任公司 | Product customization system based on social group and product customization method based on social group |
| CN105407152A (en) * | 2015-11-03 | 2016-03-16 | 竞技世界（杭州）网络技术有限公司 | Message pushing method |
| CN105677316A (en) * | 2015-12-24 | 2016-06-15 | 小米科技有限责任公司 | Message notification filtering method and apparatus |
| CN105874457A (en) * | 2016-03-30 | 2016-08-17 | 马岩 | Network information push method and system |
| CN106647645A (en) * | 2015-11-02 | 2017-05-10 | 中兴通讯股份有限公司 | Method and system for home control adjustment |
| CN107391511A (en) * | 2016-05-16 | 2017-11-24 | 中国移动通信集团内蒙古有限公司 | A kind of information-pushing method and device |
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| CN107612966A (en) * | 2017-08-11 | 2018-01-19 | 百度在线网络技术（北京）有限公司 | Feed information feedback processing methods and system |
| CN107861674A (en) * | 2017-06-27 | 2018-03-30 | 陆金所（上海）科技服务有限公司 | Financial Information method for pushing, display device and computer-readable recording medium |
| CN108476259A (en) * | 2015-12-29 | 2018-08-31 | 华为技术有限公司 | The system and method for commending contents based on user behavior |
| CN108616444A (en) * | 2018-03-30 | 2018-10-02 | 平安科技（深圳）有限公司 | A kind of PUSH message processing method, computer readable storage medium and terminal device |
| CN108846062A (en) * | 2018-06-04 | 2018-11-20 | 上海市疾病预防控制中心 | Method for pushing based on users ' individualized requirement |
| CN109063157A (en) * | 2018-08-10 | 2018-12-21 | 优视科技新加坡有限公司 | Resource recommendation method and its device, equipment/terminal/server, computer-readable medium |
| CN109165315A (en) * | 2018-08-24 | 2019-01-08 | 大陆汽车投资（上海）有限公司 | In-vehicle information/entertainment service method is pushed based on machine learning |
| CN112634881A (en) * | 2020-12-30 | 2021-04-09 | 广州博士信息技术研究院有限公司 | Intelligent voice recognition method and system based on scientific and technological achievement database |
| CN115037791A (en) * | 2022-04-13 | 2022-09-09 | 中国电信股份有限公司 | Event pushing method, device and system, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090138326A1 (en) * | 2004-11-18 | 2009-05-28 | Koninklijke Philips Electronics, N.V. | Apparatus and method for updating user profile |
| CN101520784A (en) * | 2008-02-29 | 2009-09-02 | 富士通株式会社 | Information issuing system and information issuing method |
| CN101551806A (en) * | 2008-04-03 | 2009-10-07 | 北京搜狗科技发展有限公司 | Personalized website navigation method and system |
-
2012
- 2012-05-28 CN CN2012101688700A patent/CN103455485A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090138326A1 (en) * | 2004-11-18 | 2009-05-28 | Koninklijke Philips Electronics, N.V. | Apparatus and method for updating user profile |
| CN101520784A (en) * | 2008-02-29 | 2009-09-02 | 富士通株式会社 | Information issuing system and information issuing method |
| CN101551806A (en) * | 2008-04-03 | 2009-10-07 | 北京搜狗科技发展有限公司 | Personalized website navigation method and system |
Cited By (25)
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|---|---|---|---|---|
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| CN104933163B (en) * | 2015-06-26 | 2018-03-06 | 广西中烟工业有限责任公司 | A kind of product ordering system and method based on community |
| CN106647645A (en) * | 2015-11-02 | 2017-05-10 | 中兴通讯股份有限公司 | Method and system for home control adjustment |
| CN105407152A (en) * | 2015-11-03 | 2016-03-16 | 竞技世界（杭州）网络技术有限公司 | Message pushing method |
| CN105677316A (en) * | 2015-12-24 | 2016-06-15 | 小米科技有限责任公司 | Message notification filtering method and apparatus |
| CN108476259A (en) * | 2015-12-29 | 2018-08-31 | 华为技术有限公司 | The system and method for commending contents based on user behavior |
| US10664500B2 (en) | 2015-12-29 | 2020-05-26 | Futurewei Technologies, Inc. | System and method for user-behavior based content recommendations |
| CN108476259B (en) * | 2015-12-29 | 2021-10-01 | 华为技术有限公司 | System and method for content recommendation based on user behavior |
| US11500907B2 (en) | 2015-12-29 | 2022-11-15 | Futurewei Technologies, Inc. | System and method for user-behavior based content recommendations |
| CN114138993A (en) * | 2015-12-29 | 2022-03-04 | 华为技术有限公司 | System and method for content recommendation based on user behavior |
| CN105874457A (en) * | 2016-03-30 | 2016-08-17 | 马岩 | Network information push method and system |
| WO2017166132A1 (en) * | 2016-03-30 | 2017-10-05 | 马岩 | Network information pushing method and system |
| CN107391511A (en) * | 2016-05-16 | 2017-11-24 | 中国移动通信集团内蒙古有限公司 | A kind of information-pushing method and device |
| CN107861674A (en) * | 2017-06-27 | 2018-03-30 | 陆金所（上海）科技服务有限公司 | Financial Information method for pushing, display device and computer-readable recording medium |
| CN107612966B (en) * | 2017-08-11 | 2021-01-26 | 百度在线网络技术（北京）有限公司 | Feed information feedback processing method and system |
| CN107612966A (en) * | 2017-08-11 | 2018-01-19 | 百度在线网络技术（北京）有限公司 | Feed information feedback processing methods and system |
| CN107562912A (en) * | 2017-09-12 | 2018-01-09 | 电子科技大学 | Sina weibo event recommendation method |
| CN108616444B (en) * | 2018-03-30 | 2019-10-25 | 平安科技（深圳）有限公司 | A kind of PUSH message processing method, computer readable storage medium and terminal device |
| CN108616444A (en) * | 2018-03-30 | 2018-10-02 | 平安科技（深圳）有限公司 | A kind of PUSH message processing method, computer readable storage medium and terminal device |
| CN108846062A (en) * | 2018-06-04 | 2018-11-20 | 上海市疾病预防控制中心 | Method for pushing based on users ' individualized requirement |
| CN109063157A (en) * | 2018-08-10 | 2018-12-21 | 优视科技新加坡有限公司 | Resource recommendation method and its device, equipment/terminal/server, computer-readable medium |
| CN109165315A (en) * | 2018-08-24 | 2019-01-08 | 大陆汽车投资（上海）有限公司 | In-vehicle information/entertainment service method is pushed based on machine learning |
| CN112634881A (en) * | 2020-12-30 | 2021-04-09 | 广州博士信息技术研究院有限公司 | Intelligent voice recognition method and system based on scientific and technological achievement database |
| CN112634881B (en) * | 2020-12-30 | 2023-08-11 | 广州博士信息技术研究院有限公司 | Speech intelligent recognition method and system based on scientific and technological achievement database |
| CN115037791A (en) * | 2022-04-13 | 2022-09-09 | 中国电信股份有限公司 | Event pushing method, device and system, electronic equipment and storage medium |
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