CN104424210A - Information recommendation method, information recommendation system and server - Google Patents
Information recommendation method, information recommendation system and server Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses an information recommendation system which comprises a business processing unit, a relation database and a KV database, wherein the business processing unit is used for sending history operation data of ht information to the relation database and the KV database in real time, reading the state data of the information from the relation database and the history operation data of the information from the KV database while receiving an information recommendation request, calculating a recommendation list of the information according to the history operation data of the information, the state data of the information and the user information, and returning the recommendation list; the relation database is used for calculating the state data according to the history operation data of the information; and the KV database is used for storing the history operation data of the information. The invention also discloses an information recommendation method. According to the scheme adopted by the invention, the information recommendation sensitivity is greatly improved, and the experience of the user is improved.
Description
Technical field
The present invention relates to Internet technology, be specifically related to a kind of information recommendation method, system and server.
Background technology
Along with the development of internet, applications, system can recommend the interested information of user's possibility according to the historical operation record of user, and such as user may interested song, video or commodity etc.Current information recommendation service uses the mode of calculated off-line information recommendation list: commending system every day calculates user in the set time according to the nearest trimestral full dose historical operation record of user may interested information recommendation list, Fig. 1 is the composition structural representation of information recommendation system in prior art, as shown in Figure 1, in prior art, information recommendation system comprises client 11, operation system 12 and relational database 13; Based on existing infosystem, its recommend method comprises the steps:
Client 11 asks the information of recommending to operation system 12, the recommendation request of operation system 12 customer in response end 11, returns to client 11 by information recommendation list.
Accordingly, information history service data real-time report to operation system 12, and is directed into relational database 13 once by operation system 12 every day.Such as: client 11 asks to recommend song, and the playback of songs record data of client 11 are reported relational database by operation system 12, and imports the relevant information playing song in record, as information such as the singer of song and the schools of song.
Relational database 13, according to the historical operating data of this information, calculates primary information recommendation list every day, described information recommendation list is imported operation system 12, recommends when asking for client 11 to client 11.
The shortcoming that existing information recommendation method exists comprises the following aspects:
In existing information recommendation process, information history service data is imported the information history operation note that relational database 13 common practices is once all users reporting the previous day every day every day by operation system 12, be limited to the performance of relational database 13, the process that reports of data generally needs to expend 2 hours, and relational database 13 calculates the interested information recommendation list of user, general needs 15 hours, and described information recommendation list is imported operation system 12, the general needs time of 3 hours, so, the process of a recommendation information may need the time of more than one day, that is, interested information before user asked the information of recommending to be likely two days the same day, the susceptibility recommended can be caused like this to reduce.If relational database breaks down, the result of calculation that also can cause certain day derives unsuccessfully, and the recommendation results that user can be caused like this to see today and the striking resemblances seen yesterday, can affect the experience of user like this.And upgrade proposed algorithm each time, the proving period of effect can be very long.
In addition, the number of current information recommendation list restricted information, every day can only recommend at most 100 information, if user asked the number of times of recommendation too much the same day, all information in information recommendation list were all recommended, then again can recommend the information in described information recommendation list, cause user it is seen that the information of repetition, affect the experience of user.
Summary of the invention
In view of this, fundamental purpose of the present invention is to provide a kind of information recommendation method, system and server, can calculate the interested information of user in real time and return to user's recommendation list, promotes the experience of user.
For achieving the above object, technical scheme of the present invention is achieved in that
The invention provides a kind of information recommendation system, described system comprises: Service Processing Unit, relational database and key-value (KV, Key-Value) database; Wherein,
Described Service Processing Unit, for being sent to relational database and KV database in real time by the historical operating data of information; Time also for receiving information recommendation request, the status data of described information is read from described relational database, the historical operating data of described information is read from described KV database, calculate the recommendation list of described information according to the historical operating data of described information, the status data of described information and user profile, and return described recommendation list;
Described relational database, for the historical operating data computing mode data according to described information;
Described KV database, for storing the historical operating data of described information.
In such scheme, described system also comprises user information database, for storing subscriber information, described user profile comprise following one of at least: the age of user, the sex of user, user location, the occupation of user, the hobby of user.
In such scheme, described Service Processing Unit comprises: operation system module, operational processes module, status data administration module and real-time recommendation module; Wherein,
Described operation system module, for sending the historical operating data of described information to described operational processes module; Recommendation list also for the described information described real-time recommendation module sent returns to client;
Described operational processes module, the historical operating data for the described information described operation system module sent is sent to relational database and KV database respectively;
Described status data administration module, for obtaining the status data of described information from described relational database, and sends to described real-time recommendation module by described status data;
Described real-time recommendation module, during for receiving information recommendation request, the historical operating data of described information is read from described KV database, and the described status data sent according to described historical operating data, described status data administration module and the user profile that reads from described user information database calculate the recommendation list of described information, and the recommendation list of described information is returned to operation system module.
In such scheme, the described status data that described real-time recommendation module sends according to described historical operating data, described status data administration module and the user profile that reads from described user information database calculate the recommendation list of described information, comprising:
Information in described historical operating data is carried out scoring operations according to described operation attenuation coefficient/weight table, obtains the score list of described operation information;
The score list of described operation information is associated with described information similarity, obtains the first procedure list of described recommendation list;
Obtain the related information of described user according to described customer attribute information, described related information is added into described first procedure list, obtain the second procedure list of described recommendation list;
Described second procedure list is associated with described information attribute information, obtains the recommendation list of described information.
In such scheme, described information recommendation system also comprises effect display module;
Described relational database, also for calculating the recommendation effect data of described information according to the historical operating data of described information, and sends to effect display module by described effect data;
Described effect display module, for receiving the effect data that described relational database sends, and when receiving effect and checking request, shows described effect data.
Present invention also offers a kind of information recommendation method, described method comprises:
The historical operating data of information is sent in real time relational database and KV database;
When receiving information recommendation request, from described relational database, read the status data of described information, from described KV database, read the historical data of described information; Wherein, the status data of described information is calculated by the historical operating data of described relational database according to described information;
Calculate the recommendation list of described information according to the historical operating data of described information, the status data of described information and user profile, and return described recommendation list.
In such scheme, described status data comprises: operation attenuation coefficient/weight table, information similarity and information attribute information.
In such scheme, the status data of the described historical operating data according to described information, described information and user profile calculate the recommendation list of described information, comprising:
Information in described historical operating data is carried out scoring operations according to described operation attenuation coefficient/weight table, obtains the score list of described operation information;
The score list of described operation information is associated with described information similarity, obtains the first procedure list of described recommendation list;
Obtain the related information of described user according to described customer attribute information, described related information is added into described first procedure list, obtain the second procedure list of described recommendation list;
Described second procedure list is associated with described information attribute information, obtains the recommendation list of described information.
In such scheme, described method also comprises:
Described relational database calculates the recommendation effect data of described information according to the historical operating data of described information, and when receiving effect and checking request, shows described effect data.
In such scheme, described recommendation effect data comprise: user is to the operation integrity degree of the information in described recommendation list.
Information recommendation method provided by the invention, system and server, the historical operating data of described information is sent to relational database and KV database by Service Processing Unit in real time; When receiving information recommendation request, the status data calculated according to described relational database, the historical operating data of described information read from described KV database and user profile calculate the recommendation list of described information, and return described recommendation list; So, can calculated recommendation list in real time, immediately returning user may interested information, substantially increases the sensitivity of recommendation, improves the experience of user; By by historical operating data System to relational database, effectively raise the efficiency of data summarization, make relational database can calculated recommendation effect fast, whether immediate reaction proposed algorithm be effective, so that immediately improve proposed algorithm, improve the efficiency of algorithm optimization.
Accompanying drawing explanation
Fig. 1 is the composition structural representation of information recommendation system in prior art;
Fig. 2 is the composition structural representation of the information recommendation system of the embodiment of the present invention;
Fig. 3 is the schematic flow sheet of the information recommendation method of the embodiment of the present invention;
Fig. 4 is the schematic diagram of the computing method of the recommendation list of the embodiment of the present invention.
Embodiment
Basic thought of the present invention is: the recommendation list calculating described information after receiving the information recommendation request of client in real time, and to historical operating data and status data separate management, by read operation KV database purchase historical operating data more efficiently, by relational database store status data, when the list of needs calculated recommendation, historical operating data is read respectively from KV database, reads status data from relational database, according to described historical operating data, described status data and the list of user profile calculated recommendation.
Below in conjunction with drawings and the specific embodiments, the present invention is further described in more detail.
Fig. 2 is the composition structural representation of the information recommendation system of the embodiment of the present invention, and as shown in Figure 2, described information recommendation system mainly comprises: Service Processing Unit 21, relational database 22 and KV database 23; Wherein,
Described Service Processing Unit 21, for being sent to relational database 22 and KV database 23 in real time by the historical operating data of described information; Time also for receiving information recommendation request, the status data of described information is read from described relational database 22, the historical operating data of described information is read from described KV database 23, calculate the recommendation list of described information according to the historical operating data of described information, the status data of described information and user profile, and return described recommendation list;
Described relational database 22, for the historical operating data computing mode data according to described information;
Described KV database 23, for storing the historical operating data of described information.
Preferably, described system also comprises user information database 24, for storing subscriber information, described user profile comprise following one of at least: the age, sex, province, user place, the occupation of user, the hobby of user etc. of user.
Preferably, described Service Processing Unit 21 comprises: operation system module 211, operational processes module 212, status data administration module 213 and real-time recommendation module 214; Wherein,
Described operation system module 211, for sending the historical operating data of described information to described operational processes module 212; Recommendation list also for the described information described real-time recommendation module 214 sent returns to client;
Described operational processes module 212, the historical operating data for the described information described operation system module 211 sent is sent to relational database 22 and KV database 23 respectively;
Described status data administration module 213, for obtaining the status data of described information from described relational database 22, and sends to described real-time recommendation module 214 by described status data;
Described real-time recommendation module 214, during for receiving information recommendation request, the historical operating data of described information is read from described KV database 23, and calculate the recommendation list of described information according to described historical operating data, the described status data of described status data administration module 213 transmission and the user profile of reading from described user information database 24, and the recommendation list of described information is returned to operation system module 211.
Preferably, described information recommendation system also comprises effect display module 25,
Described relational database 22, also for calculating the recommendation effect data of described information according to the historical operating data of described information, and sends to effect display module 25 by described effect data;
Described effect display module 25, for receiving the effect data that described relational database 22 sends, and when receiving effect and checking request, shows described effect data.
Wherein, the Service Processing Unit 21 of described information recommendation system in actual applications, can by central processing unit (the Central Processing Unit in system, or digital signal processor (Digital SignalProcessor CPU), DSP) or programmable gate array (Field-Programmable Gate Array, FPGA) realize; The KV database 23 of described information recommendation system and user information database 24 in actual applications, all can be realized by the storer in system; In actual applications, its data processing function can be realized by CPU or DSP in system or FPGA the relational database 22 of described information recommendation system, and its memory function can be realized by storer; The effect display module 25 of described information recommendation system in actual applications, can be realized by display.
The embodiment of the present invention additionally provides a kind of server, and described server comprises the information recommendation system described in the embodiment of the present invention.
Based on above-mentioned information recommendation system, embodiments provide a kind of information recommendation method, as shown in Figure 3, Fig. 3 is the schematic flow sheet of the information recommendation method of the embodiment of the present invention, comprises the following steps:
Step 301: the historical operating data of information is sent to relational database in real time.
Described relational database calculates the status data of described information according to the historical operating data of described information.Here, described status data comprises: operation attenuation coefficient/weight table, information similarity and information attribute information.
Concrete, described relational database can arrange described attenuation coefficient/weight table according to the generation time of data in described historical operating data and the preference of user, and the time that data produce, more early coefficient/weighted value was lower, otherwise then coefficient/weighted value is higher; The number of times of information operating is more, and coefficient/weighted value is higher, otherwise then coefficient/weighted value is lower; Described information similarity is one and is greater than zero decimal being less than, more more close close to 1, the numerical value of described information similarity can according to being the label acquisition that described information is arranged in advance, be that described information arranges label according to the attribute information of described information, identical label is more, then more similar, for music, be that music arranges label according to the attribute information of music, such as label is set to: 85-95 age, piano, express one's emotion; Described information attribute information can obtain according to the historical operating data of user, such as according to the information attribute liked and do not like of the historical operating data acquisition user of user, to recommend music, described information attribute information can comprise the attribute information such as school, the languages of music, the singer of music of music.
Step 302 ~ step 303: when receiving information recommendation request, the status data of described information is read from described relational database, from described KV database, read the historical data of described information, calculate the recommendation list of described information according to the status data of described information, the historical operating data of described information and user profile and return recommendation list.
Preferably, the historical operating data of the described status data according to information, described information and user profile calculate the recommendation list of described information, comprising:
Information in described historical operating data is carried out scoring operations according to described operation attenuation coefficient/weight table, obtains the score list of described operation information;
The score list of described operation information is associated with described information similarity, obtains the first procedure list of described recommendation list;
Obtain the related information of described user according to described customer attribute information, described related information is added into described first procedure list, obtain the second procedure list of described recommendation list;
Described second procedure list is associated with described information attribute information, obtains the recommendation list of described information.
Ask to recommend the realization flow of song to embodiment of the present invention information recommendation method to be described in further detail for user below.
User logs in client by computer or mobile phone, such as log in QQ music client end, described operation system module sends the historical operating data of this user in real time to operational processes module, comprise listen to music, search music etc., described historical operating data is sent to relational database and KV database by operational processes module respectively;
Described relational database is according to described historical operating data by preset rules computing mode data, and concrete, described status data comprises: operation attenuation coefficient/weight table, information similarity and information attribute information etc.Described relational database can arrange described attenuation coefficient/weight table according to the generation time of data in described historical operating data and the preference of user, and the time that data produce, more early coefficient/weighted value was lower, otherwise then coefficient/weighted value is higher; The number of times that identical music is listened to or searched for is more, and coefficient/weighted value is higher, otherwise then coefficient/weighted value is lower; Described information similarity is one and is greater than zero decimal being less than, more more close close to 1, the numerical value of described information similarity can according to being the label acquisition that described information is arranged in advance, be that described information arranges label according to the attribute information of described information, identical label is more, then more similar, for music, be that music arranges label according to the attribute information of music, such as label is set to: 85-95 age, piano, express one's emotion; Described information attribute information can obtain according to the historical operating data of user, such as according to the information attribute liked and do not like of the historical operating data acquisition user of user, comprises the attribute informations such as the school of music, the languages of music, the singer of music.
Described KV database purchase has the historical operating data of user to some extent;
When user logs in client-requested recommendation music, client sends the music recommend request of this user to operation system module, and real-time recommendation module calculates the music recommend list of this user; Status data administration module reads the status data of this user from described relational database, and described status data comprises operation attenuation coefficient/weight table, information similarity, information attribute information etc., and described status data is sent to described real-time recommendation module; Described real-time recommendation module reads the historical operating data of this user from described KV database, according to the user profile calculated recommendation list of described historical operating data, described status data and this user.Concrete, Fig. 4 is the schematic diagram of the computing method of the recommendation list of the embodiment of the present invention, as shown in Figure 4, comprises the steps:
Song in historical operating data, according to operation attenuation coefficient/weight table, is given a mark by described real-time recommendation module, obtains the score list of operation song; The score list of described operation song is associated with information similarity, obtains recommendation list 1;
If described recommendation list 1 is empty, or the number of songs in described recommendation list 1 is few, then according to the song that customer attribute information acquisition user may like, such as according to the age of user, the information such as occupation, user location of user, other users that retrieval is similar with active user, the song that other users described like is added in the recommendation list of active user, obtains recommendation list 2;
Described recommendation list 2 is associated with information attribute information, described information attribute information comprises the song attributes information that active user does not like, comprise: the attribute informations such as the singer of the school of music, the languages of music, music, if active user does not like the music of a certain singer, then the music of described singer is deleted from described recommendation list 2; In addition, for the music that active user had operated recently, delete, to recommend new music to user simultaneously; After deleting corresponding music, obtain recommendation list 3;
Described recommendation list 3 is associated with information attribute information 2, described information attribute information is identical with the information attribute information in previous step, object is the music in described recommendation list 3 to carry out adjustment operation, adjust the music not occurring identical singer in described recommendation list 3 in continuous print at least ten song, after corresponding adjustment operation terminates, obtain final recommendation list.
Described recommendation list is back to described operation system module, and returns to client in time by described operation system module after calculating and obtaining final recommendation list by described real-time recommendation module.
Preferably, this step also comprises: described relational database calculates the recommendation effect data of described information according to the historical operating data of described information, and when receiving effect and checking request, shows described effect data.
Preferably, described recommendation effect data comprise: user is to the operation integrity degree of the information in described recommendation list.
Here, described recommendation effect data can comprise several data, to recommend music, can be user listen to ratio to the music in music recommend list complete, on average listen to duration, on average listen to number, listen to 60% accounting of music, 30% accounting listening to music etc. for the operation integrity degree of the information in described recommendation list; To recommend video, then can be user watch ratio to the video in video recommendations list complete, on average watch duration, on average watch the data such as number the information operating integrity degree in described recommendation list; Can find out from these data whether the information of recommendation is liked by user, thus can reflect that whether current proposed algorithm is effective.
Concrete, within a period of time, described relational database calculates the recommendation effect data of music according to receiving described historical operating data, described effect data is sent to effect plays module, and when maintainer checks described effect data, show described effect data.Described recommendation effect data are specifically reflected as the recommendation effect data to current proposed algorithm, such as user is complete on the music recommend page listens to ratio, on average listen to duration, on average listen to number etc., can find out from these data whether the music of recommendation is liked by user, thus can reflect that whether current proposed algorithm is effective.
The recommendation effect list that table 1 obtains for actual computation, as shown in table 1, what player deep bid represented is all old song form of listening at QQ music client end is; What real time correlation recommended expression is when user has operation history, for the feedback of recommending music; What real-time property was recommended to represent is user when there is no historical operating data, for recommending the feedback of music.As can be seen from Table 1, by completely listening song ratio, 60% accounting listening to song, 30% accounting listening to song, on average listening song duration, on average listen these indexs such as song number, whether all can react user to like the music of recommending, numeral is larger, illustrates that user more likes.
Table 1
If the information recommendation method described in the embodiment of the present invention using the form of software function module realize and as independently production marketing or use time, also can be stored in a computer read/write memory medium.Based on such understanding, the technical scheme of the embodiment of the present invention can embody with the form of software product the part that prior art contributes in essence in other words, this computer software product is stored in a storage medium, comprises some instructions and performs all or part of of method described in each embodiment of the present invention in order to make a computer equipment (can be personal computer, server or the network equipment etc.).And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. various can be program code stored medium.Like this, the embodiment of the present invention is not restricted to any specific hardware and software combination.
Accordingly, the embodiment of the present invention also provides a kind of computer-readable storage medium, wherein stores computer program, and this computer program is for performing the information recommendation method of the embodiment of the present invention.
The above, be only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.All any amendments done within the spirit and scope of the present invention, equivalent replacement and improvement etc., be all included within protection scope of the present invention.
Claims (10)
1. an information recommendation system, is characterized in that, described system comprises: Service Processing Unit, relational database and key-value (KV) database; Wherein,
Described Service Processing Unit, for being sent to relational database and KV database in real time by the historical operating data of information; Time also for receiving information recommendation request, the status data of described information is read from described relational database, the historical operating data of described information is read from described KV database, calculate the recommendation list of described information according to the historical operating data of described information, the status data of described information and user profile, and return described recommendation list;
Described relational database, for the historical operating data computing mode data according to described information;
Described KV database, for storing the historical operating data of described information.
2. information recommendation system according to claim 1, it is characterized in that, described system also comprises user information database, for storing subscriber information, described user profile comprise following one of at least: the age of user, the sex of user, user location, the occupation of user, the hobby of user.
3. information recommendation system according to claim 1, is characterized in that, described Service Processing Unit comprises: operation system module, operational processes module, status data administration module and real-time recommendation module; Wherein,
Described operation system module, for sending the historical operating data of described information to described operational processes module; Recommendation list also for the described information described real-time recommendation module sent returns to client;
Described operational processes module, the historical operating data for the described information described operation system module sent is sent to relational database and KV database respectively;
Described status data administration module, for obtaining the status data of described information from described relational database, and sends to described real-time recommendation module by described status data;
Described real-time recommendation module, during for receiving information recommendation request, the historical operating data of described information is read from described KV database, and the described status data sent according to described historical operating data, described status data administration module and the user profile that reads from described user information database calculate the recommendation list of described information, and the recommendation list of described information is returned to operation system module.
4. information recommendation system according to claim 3, it is characterized in that, the described status data that described real-time recommendation module sends according to described historical operating data, described status data administration module and the user profile that reads from described user information database calculate the recommendation list of described information, comprising:
Information in described historical operating data is carried out scoring operations according to described operation attenuation coefficient/weight table, obtains the score list of described operation information;
The score list of described operation information is associated with described information similarity, obtains the first procedure list of described recommendation list;
Obtain the related information of described user according to described customer attribute information, described related information is added into described first procedure list, obtain the second procedure list of described recommendation list;
Described second procedure list is associated with described information attribute information, obtains the recommendation list of described information.
5. information recommendation system according to claim 1, is characterized in that, described information recommendation system also comprises effect display module;
Described relational database, also for calculating the recommendation effect data of described information according to the historical operating data of described information, and sends to effect display module by described effect data;
Described effect display module, for receiving the effect data that described relational database sends, and when receiving effect and checking request, shows described effect data.
6. an information recommendation method, is characterized in that, described method comprises:
The historical operating data of information is sent in real time relational database and key-value (KV) database;
When receiving information recommendation request, from described relational database, read the status data of described information, from described KV database, read the historical data of described information; Wherein, the status data of described information is calculated by the historical operating data of described relational database according to described information;
Calculate the recommendation list of described information according to the historical operating data of described information, the status data of described information and user profile, and return described recommendation list.
7. information recommendation method according to claim 6, is characterized in that, described status data comprises: operation attenuation coefficient/weight table, information similarity and information attribute information.
8. the information recommendation method according to claim 6 or 7, is characterized in that, the status data of the described historical operating data according to described information, described information and user profile calculate the recommendation list of described information, comprising:
Information in described historical operating data is carried out scoring operations according to described operation attenuation coefficient/weight table, obtains the score list of described operation information;
The score list of described operation information is associated with described information similarity, obtains the first procedure list of described recommendation list;
Obtain the related information of described user according to described customer attribute information, described related information is added into described first procedure list, obtain the second procedure list of described recommendation list;
Described second procedure list is associated with described information attribute information, obtains the recommendation list of described information.
9. method according to claim 6, is characterized in that, described method also comprises:
Described relational database calculates the recommendation effect data of described information according to the historical operating data of described information, and when receiving effect and checking request, shows described effect data.
10. information recommendation method according to claim 9, is characterized in that, described recommendation effect data comprise: user is to the operation integrity degree of the information in described recommendation list.
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| CN107180086A (en) * | 2017-05-09 | 2017-09-19 | 中国石油集团川庆钻探工程有限公司 | A kind of drilling well real time data quick storage and dissemination method |
| WO2017193897A1 (en) * | 2016-05-12 | 2017-11-16 | 腾讯科技（深圳）有限公司 | Data recommendation method and device therefor, and storage medium |
| WO2017206604A1 (en) * | 2016-05-30 | 2017-12-07 | 阿里巴巴集团控股有限公司 | Processing and interaction method for use in data recommendation, device, and system |
| CN107547740A (en) * | 2017-08-28 | 2018-01-05 | 江西博瑞彤芸科技有限公司 | The management method and system of station list |
| CN108076353A (en) * | 2017-05-18 | 2018-05-25 | 北京市商汤科技开发有限公司 | Business object recommends method, apparatus, storage medium and electronic equipment |
| CN110019925A (en) * | 2017-12-07 | 2019-07-16 | 北京雷客天地科技有限公司 | Batch deletes the method and device of song |
| CN111291018A (en) * | 2018-12-07 | 2020-06-16 | 北京沃东天骏信息技术有限公司 | Data management method, device, equipment and storage medium |
| CN116828535A (en) * | 2023-08-30 | 2023-09-29 | 太一云境技术有限公司 | Audio sharing method and system based on wireless transmission |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110125753A1 (en) * | 2009-11-20 | 2011-05-26 | Rovi Technologies Corporation | Data delivery for a content system |
| CN102332006A (en) * | 2011-08-03 | 2012-01-25 | 百度在线网络技术（北京）有限公司 | Information push control method and device |
| CN102654860A (en) * | 2011-03-01 | 2012-09-05 | 北京彩云在线技术开发有限公司 | Personalized music recommendation method and system |
| CN103049865A (en) * | 2012-12-17 | 2013-04-17 | 中国农业大学 | Method and system for initiatively recommending product information service |
-
2013
- 2013-08-22 CN CN201310369757.3A patent/CN104424210B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110125753A1 (en) * | 2009-11-20 | 2011-05-26 | Rovi Technologies Corporation | Data delivery for a content system |
| CN102654860A (en) * | 2011-03-01 | 2012-09-05 | 北京彩云在线技术开发有限公司 | Personalized music recommendation method and system |
| CN102332006A (en) * | 2011-08-03 | 2012-01-25 | 百度在线网络技术（北京）有限公司 | Information push control method and device |
| CN103049865A (en) * | 2012-12-17 | 2013-04-17 | 中国农业大学 | Method and system for initiatively recommending product information service |
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|---|---|---|---|---|
| CN106708829A (en) * | 2015-07-31 | 2017-05-24 | 腾讯科技（深圳）有限公司 | Data recommendation method and data recommendation system |
| CN105512934A (en) * | 2015-10-22 | 2016-04-20 | 深圳怡化电脑股份有限公司 | Method and device for inputting business parameters |
| CN106803178B (en) * | 2015-11-26 | 2020-09-18 | 阿里巴巴集团控股有限公司 | Method and equipment for processing entity |
| CN106803178A (en) * | 2015-11-26 | 2017-06-06 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of processing entities |
| CN106844359A (en) * | 2015-12-04 | 2017-06-13 | 深圳富泰宏精密工业有限公司 | Server and its music service system and method |
| US10706363B2 (en) | 2016-05-12 | 2020-07-07 | Tencent Technology (Shenzhen) Company Limited | Data recommendation method and device, and storage medium |
| WO2017193897A1 (en) * | 2016-05-12 | 2017-11-16 | 腾讯科技（深圳）有限公司 | Data recommendation method and device therefor, and storage medium |
| CN107451141A (en) * | 2016-05-30 | 2017-12-08 | 阿里巴巴集团控股有限公司 | Processing exchange method, the apparatus and system of a kind of data recommendation |
| WO2017206604A1 (en) * | 2016-05-30 | 2017-12-07 | 阿里巴巴集团控股有限公司 | Processing and interaction method for use in data recommendation, device, and system |
| CN106210127B (en) * | 2016-08-15 | 2019-07-16 | 腾讯科技（深圳）有限公司 | A kind of information processing method, server and client |
| CN106210127A (en) * | 2016-08-15 | 2016-12-07 | 腾讯科技（深圳）有限公司 | A kind of information processing method, server and client |
| CN107180086A (en) * | 2017-05-09 | 2017-09-19 | 中国石油集团川庆钻探工程有限公司 | A kind of drilling well real time data quick storage and dissemination method |
| CN108076353A (en) * | 2017-05-18 | 2018-05-25 | 北京市商汤科技开发有限公司 | Business object recommends method, apparatus, storage medium and electronic equipment |
| CN107547740A (en) * | 2017-08-28 | 2018-01-05 | 江西博瑞彤芸科技有限公司 | The management method and system of station list |
| CN110019925A (en) * | 2017-12-07 | 2019-07-16 | 北京雷客天地科技有限公司 | Batch deletes the method and device of song |
| CN111291018A (en) * | 2018-12-07 | 2020-06-16 | 北京沃东天骏信息技术有限公司 | Data management method, device, equipment and storage medium |
| CN116828535A (en) * | 2023-08-30 | 2023-09-29 | 太一云境技术有限公司 | Audio sharing method and system based on wireless transmission |
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