CN104991900A - Method and apparatus for pushing music data - Google Patents
Method and apparatus for pushing music data Download PDFInfo
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- CN104991900A CN104991900A CN201510312808.8A CN201510312808A CN104991900A CN 104991900 A CN104991900 A CN 104991900A CN 201510312808 A CN201510312808 A CN 201510312808A CN 104991900 A CN104991900 A CN 104991900A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/63—Querying
- G06F16/635—Filtering based on additional data, e.g. user or group profiles
- G06F16/637—Administration of user profiles, e.g. generation, initialization, adaptation or distribution
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Abstract
The application discloses a method and an apparatus for pushing music data, wherein the method comprises: acquiring stream datum of songs listened by a user and calculating scores of each of the songs listened by the user according to the stream datum; the song score is proportional to the display count and is inversely proportional to the length between the display time point of a song and the current display time point; the method also comprises: selecting a song of which the score is higher than a first threshold value as a song in which the user is interested; and pushing an album and a song menu that have preset associated relationship with the song in which the user is interested to the user. According to the application, by combining the stream datum of the songs listened by the user, the song in which the user is interested are acquired; and the album and the song menu related to the song in which the user is interested are pushed to the user, thus the pushing result is more preferable to the user; and the user experience degree is enhanced.
Description
Technical field
The application relates to networking technology area, more particularly, relates to a kind of music data method for pushing and device.
Background technology
Along with the development of development of Mobile Internet technology, terminal provides more and more diversified application, such as music, ecommerce, electronic navigation etc.Wherein, music application software for user enjoy music provide convenient greatly.
Existing music application software all provides music push function, by pushing special edition to user and song is single, and can the convenient user music that finds oneself to like.But present inventor finds through research, existing music pushes mode and is mainly directly pushed to each user by single to current the hottest up-to-date special edition and song, and does not consider that the difference of different user listens song hobby, and therefore propelling movement result is unsatisfactory.
Summary of the invention
In view of this, this application provides a kind of music data method for pushing and device, not considering that the difference of different user listens song hobby for solving existing music data method for pushing, thus causing propelling movement weak effect, the problem that user experience is low.
To achieve these goals, the existing scheme proposed is as follows:
A kind of music data method for pushing, comprising:
Obtain the history of user and listen song pipelined data, described history listens song pipelined data to comprise listened to the music song information, the broadcasting time of each song and play time;
Listen song pipelined data described in foundation, calculate the mark of each song that user listens, wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song;
Filter out the song that song score exceedes first threshold, be defined as user's song interested;
To with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
A kind of music data pusher, comprising:
Data capture unit, the history for obtaining user listens song pipelined data, and described history listens song pipelined data to comprise listened to the music song information, the broadcasting time of each song and play time;
Song score computing unit, for listening song pipelined data described in foundation, calculate the mark of each song that user listens, wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song;
Song determining unit interested, exceedes the song of first threshold for filtering out song score, be defined as user's song interested;
First push unit, for having the special edition of preset incidence relation with described user song interested and sing and be singly pushed to user.
As can be seen from above-mentioned technical scheme, the music data method for pushing that the embodiment of the present application provides, song pipelined data is listened by obtaining user's history, and calculate the mark of each song that user listens accordingly, wherein song score is directly proportional to the broadcasting time of song, be inversely proportional to the length of the play time distance current point in time of song, filter out the song that song score exceedes first threshold, as user's song interested, and will with user's song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.The application's methods combining user history listens song flowing water to draw user's song interested, and then pushes the special edition that associate with this song interested to user and sing singly, makes propelling movement result be more prone to the true hobby of being close to the users, improves user experience.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only the embodiment of the application, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to the accompanying drawing provided.
Fig. 1 is a kind of music data method for pushing process flow diagram disclosed in the embodiment of the present application;
Fig. 2 is another kind of music data method for pushing process flow diagram disclosed in the embodiment of the present application;
Fig. 3 is the embodiment of the present application another music data method for pushing process flow diagram disclosed;
Fig. 4 is the embodiment of the present application another music data method for pushing process flow diagram disclosed;
Fig. 5 is a kind of music data pusher structural representation disclosed in the embodiment of the present application;
Fig. 6 is a kind of song score computing unit structural representation disclosed in the embodiment of the present application;
Fig. 7 is a kind of song score addition unit structural representation disclosed in the embodiment of the present application;
Fig. 8 is another kind of song score addition unit structural representation disclosed in the embodiment of the present application;
Fig. 9 is another kind of music data pusher structural representation disclosed in the embodiment of the present application;
Figure 10 is the embodiment of the present application another music data pusher structural representation disclosed;
Figure 11 is a kind of server hardware structural representation disclosed in the embodiment of the present application.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present application, be clearly and completely described the technical scheme in the embodiment of the present application, obviously, described embodiment is only some embodiments of the present application, instead of whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of the application's protection.
See Fig. 1, Fig. 1 a kind of music data method for pushing process flow diagram disclosed in the embodiment of the present application.
As shown in Figure 1, the method comprises:
The history of step S100, acquisition user listens song pipelined data;
History listen song pipelined data can be apart from current time one month, two months or in other period of cycle user listen song pipelined data.
History listens song pipelined data can comprise listened to the music song information, the broadcasting time of each song and play time.Song information can comprise the information such as style, song duration of the title of song, song artist's (also i.e. singer), song.For the broadcasting time of each song, by total playing duration divided by song duration calculation, or otherwise record can be carried out.
Listen song pipelined data described in step S110, foundation, calculate the mark of each song that user listens;
Wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song.Simply in other words, if the number of times that a song user plays is more, then represent this song score higher, play time is longer apart from current point in time, then represent its mark lower, and the song of listening that this point also exactly meets people is accustomed to.
Citing as, user has altogether listened 3 songs within a week, wherein the broadcasting time of each song and play time as shown in the table:
Table 1
As seen from the above table, for song A, its broadcasting time is maximum, and play time is relatively near current point in time; For song B, its broadcasting time is minimum, and play time is also distant from current point in time; For song C, its broadcasting time is placed in the middle, and play time distance current point in time is more placed in the middle.Therefore, the score rank of three songs is song A score > song C score > song B score.
Step S120, filter out the song that song score exceedes first threshold, be defined as user's song interested;
Particularly, song score lower limit can be preset, i.e. first threshold, select song score and exceed the song of first threshold as the interested song of user.
Step S130, will with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
Wherein, preset incidence relation can have multiple strategy, such as, song place interested for user special edition is set as song interested possesses incidence relation with user, is sung at song place interested for user to set up and be decided to be the song interested with user and possess incidence relation etc.
The music data method for pushing that the embodiment of the present application provides, song pipelined data is listened by obtaining user's history, and calculate the mark of each song that user listens accordingly, wherein song score is directly proportional to the broadcasting time of song, be inversely proportional to the length of the play time distance current point in time of song, filter out the song that song score exceedes first threshold, as user's song interested, and will with user's song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.The application's methods combining user history listens song flowing water to draw user's song interested, and then pushes the special edition that associate with this song interested to user and sing singly, makes propelling movement result be more prone to the true hobby of being close to the users, improves user experience.
See Fig. 2, Fig. 2 another kind of music data method for pushing process flow diagram disclosed in the embodiment of the present application.
As shown in Figure 2, the method comprises:
The history of step S200, acquisition user listens song pipelined data;
History listens song pipelined data can comprise listened to the music song information, the broadcasting time of each song and play time.Song information can comprise the information such as style, song duration of the title of song, song artist's (also i.e. singer), song.For the broadcasting time of each song, by total playing duration divided by song duration calculation, or otherwise record can be carried out.
Step S210, by preset time period in units of, to described history listen song pipelined data divide, the history obtaining some time section listens song pipelined data;
Particularly, history listens song pipelined data can be user apart from the current time previous moon, two months or the data At All Other Times in section.Listen song pipelined data for this phase of history, we can be divided into multiple subsegment history and be listened song pipelined data, can be that unit divides according to the time period of presetting during division.Citing as, user's history listen song pipelined data be the previous moon listen song pipelined data.We can in units of sky, by one month listen song pipelined data to be divided into 30 days (supposing that this moon has 30 days) listen song pipelined data.
Step S220, listen song pipelined data to carry out the calculating of song score for the history of each time period, and the score of same song in each time period to be added, to obtain the gross score of each song;
After dividing according to the time period, the history obtaining multiple time period listens song pipelined data.In each time period, song that user listens is not necessarily identical, and same song may by user's program request within the different time periods.Therefore, listen each song in song pipelined data to carry out score calculating for history in each time period, finally the score of same song in different time sections be added, result is the gross score of this song.
Step S230, filter out the song that song score exceedes first threshold, be defined as user's song interested;
Particularly, song score lower limit can be preset, i.e. first threshold, select song score and exceed the song of first threshold as the interested song of user.
Step S240, will with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
Compared to a upper embodiment, present embodiment discloses a kind of foundation listen song pipelined data calculate user listen the method for each first song score.For the ease of understanding said process, be described by following example.
We are described with user's song pipelined data of listening of nearest a week.Its data are listed in the table below:
Table 2
Can determine the scoring event of each song in each sky by table 2, the score of same song in each sky is added by we, can determine the gross score of each song:
Song A score=30+60=90;
Song B score=35+35+66=136;
Song C score=40+50=90;
Song D score=54+70=124;
Song E score=20+60=80.
In another embodiment of the application, we introduce above-mentioned steps S220, listen for the history of each time period song pipelined data to carry out the calculating of song score, and the score of same song in each time period is added, the one obtaining the gross score of each song can embodiment.
Provide a kind of song score computing formula in the present embodiment, the score that the history for each time period listens song pipelined data to carry out each song calculates:
Score
ti＝playcount
ti*e
-λt
Wherein, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date.
User's history that initial stage obtains listens all songs in song pipelined data to be numbered by we, and (1, n) wherein, n listens for user's history and sings song total number in pipelined data i ∈.
As for broadcasting time playcount
ti, it can by the total playing duration of calculating i-th song within t the time period before current date, and the song duration divided by the i-th song is determined.
After calculating the scoring event of complete each song within each time period, the score of same song within each time period sued for peace, result is the gross score of this song:
In addition, consider that existing music site or application all provide the function of song collection, the song that user can like oneself is collected, so that follow-up appreciation.One song is collected representative of consumer by user and is enjoyed a lot it, and therefore in another embodiment of the application, we consider song collection situation to add in the process of song score calculating, specific as follows:
History listens song pipelined data also to comprise collecting state and the collection time point of each song, then the process of listening song pipelined data to carry out the calculating of song score for the history of each time period can comprise:
History for each time period listens song pipelined data, calculates the score of each song according to the following equation:
Score
ti＝(playcount
ti+kβ)*e
-λt
Wherein, when the i-th song k value when t time period is collected by user before current date is 1, when the i-th song k value when t time period is not collected by user before current date is 0, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date.
Known compared to a upper embodiment, in this enforcement, song score computing formula with the addition of the influence factor of song collection state.If a song has been collected by user within the corresponding time period, then need in the score computing formula of this song within the corresponding time period to increase collection factor influence value β, β value can set according to actual conditions.
For the process that same song is added in the score of each time period, it is identical with a upper embodiment, according to following formulae discovery:
In another embodiment of the application, disclose another music data method for pushing.Be the embodiment of the present application another music data method for pushing process flow diagram disclosed see Fig. 3, Fig. 3.
As shown in Figure 3, the method comprises:
The history of step S300, acquisition user listens song pipelined data;
Listen song pipelined data described in step S310, foundation, calculate the mark of each song that user listens;
Wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song.Simply in other words, if the number of times that a song user plays is more, then represent this song score higher, play time is longer apart from current point in time, then represent its mark lower, and the song of listening that this point also exactly meets people is accustomed to.
Step S320, filter out the song that song score exceedes first threshold, be defined as user's song interested;
Particularly, song score lower limit can be preset, i.e. first threshold, select song score and exceed the song of first threshold as the interested song of user.
Step S330, will with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user;
Step S340, with style belonging to song for class condition, each song that user listens is classified;
Particularly, according to the style that user's history listens the song style in song pipelined data in song information can determine belonging to each song, with style by class condition is listened each song to classify to user.
Step S350, the summation of the mark of song each under same style is defined as the mark of this style;
Step S360, filter out the style that style mark exceedes Second Threshold, be defined as user's style interested;
Step S370, will with user's style interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
Wherein, preset incidence relation can have multiple strategy, such as, special edition the highest for mark under style interested for user or song is set up and is decided to be the style interested with user and possesses incidence relation etc.Wherein, special edition or the single mark of song are majority of network user to special edition or the single scoring of song, represent the network user to its fancy grade.
It should be noted that, above-mentioned steps S340-S370 can optional position after step S310, and Fig. 3 merely illustrates a kind of situation.
In the present embodiment, according to user listen the mark of song, determine the song style interested to user further, and then can push to user the special edition that is associated with style interested and sing singly.
It should be noted that, when pushing association song list according to user's song style interested to user, can determine according to the genre labels that song is single to push target, song identical with user's song interested style for the single style of song is singly pushed to user.But, consider that the genre labels of song list is artificial setting, likely there is the situation of mislabeling, therefore can sing in antiphonal style single in song style confirm, confirm song single in the style of a certain proportion of song be user's song style interested time ability push this song list to user.
In another embodiment of the application, disclose another music data method for pushing.Be the embodiment of the present application another music data method for pushing process flow diagram disclosed see Fig. 4, Fig. 4.
As shown in Figure 4, the method comprises:
The history of step S400, acquisition user listens song pipelined data;
Listen song pipelined data described in step S410, foundation, calculate the mark of each song that user listens;
Wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song.Simply in other words, if the number of times that a song user plays is more, then represent this song score higher, play time is longer apart from current point in time, then represent its mark lower, and the song of listening that this point also exactly meets people is accustomed to.
Step S420, filter out the song that song score exceedes first threshold, be defined as user's song interested;
Particularly, song score lower limit can be preset, i.e. first threshold, select song score and exceed the song of first threshold as the interested song of user.
Step S430, will with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user;
Step S440, with the singer of song for class condition, each song that user listens is classified;
Particularly, the singer in song pipelined data in song information is listened can to determine the singer of each song, with singer by class condition is listened each song to classify to user according to user's history.
Step S450, the summation of the mark of song each under same singer is defined as the mark of this singer;
Step S460, filter out the singer of singer's mark more than the 3rd threshold value, be defined as user singer interested;
Step S470, will with user singer interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
Wherein, preset incidence relation can have multiple strategy, such as, the latest album that singer interested for user issues is set as that singer interested possesses incidence relation etc. with user.
It should be noted that, above-mentioned steps S440-S470 can optional position after the step s 410, and Fig. 4 merely illustrates a kind of situation.
In the present embodiment, according to user listen the mark of song, determine the singer interested to user further, and then can push to user the special edition that is associated with singer interested and sing singly.
Known in conjunction with above-mentioned several embodiment, the application according to user's song interested, user's style interested and user singer interested three factors, can push special edition and song list to user.Three kinds push mode can combination in any, this application is not limited.
The process of mode combination in any is pushed at above-mentioned three kinds, the situation that the special edition that the two or three propelling movement mode that likely occurs pushes is multiple with song substance, the special edition that then now can push three kinds of modes and song singly carry out duplicate removal process, and then carry out the propelling movement of special edition and song list.
Be described the music data pusher that the embodiment of the present application provides below, music data pusher described below can mutual corresponding reference with above-described music data method for pushing.
See Fig. 5, Fig. 5 a kind of music data pusher structural representation disclosed in the embodiment of the present application.
As shown in Figure 5, this device comprises:
Data capture unit 51, the history for obtaining user listens song pipelined data, and described history listens song pipelined data to comprise listened to the music song information, the broadcasting time of each song and play time;
Song score computing unit 52, for listening song pipelined data described in foundation, calculate the mark of each song that user listens, wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song;
Song determining unit 53 interested, exceedes the song of first threshold for filtering out song score, be defined as user's song interested;
First push unit 54, for having the special edition of preset incidence relation with described user song interested and sing and be singly pushed to user.
Optionally, Fig. 6 illustrates a kind of alternate configurations of above-mentioned song score computing unit 52, and as shown in Figure 6, song score computing unit 52 can comprise:
Data dividing unit 521, in units of the time period of presetting, listen song pipelined data to divide to described history, the history obtaining some time section listens song pipelined data;
Song score addition unit 522, for listening for the history of each time period song pipelined data to carry out the calculating of song score, and being added the score of same song in each time period, obtaining the gross score of each song.
Optionally, Fig. 7 illustrates a kind of alternate configurations of above-mentioned song score addition unit 522, and as shown in Figure 7, song score addition unit 522 can comprise:
First computing unit 5221, for listening song pipelined data for the history of each time period, calculates the score of each song according to the following equation:
Score
ti＝playcount
ti*e
-λt
Wherein, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date;
Second computing unit 5222, for calculating same song in the score of each time period and value:
Optionally, described history listens song pipelined data can also comprise collecting state and the collection time point of each song, then as shown in Figure 8, song score addition unit 522 can comprise:
3rd computing unit 5223, for listening song pipelined data for the history of each time period, calculates the score of each song according to the following equation:
Score
ti＝(playcount
ti+kβ)*e
-λt
Wherein, when the i-th song k value when t time period is collected by user before current date is 1, when the i-th song k value when t time period is not collected by user before current date is 0, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date;
4th computing unit 5224, for calculating same song in the score of each time period and value:
Optionally, Fig. 9 illustrates the another kind of alternate configurations of the application's music data pusher, composition graphs 5 and Fig. 9 known, music data pusher can also comprise:
Genre classification unit 55, for style belonging to song for class condition, each song that user listens is classified;
Style score calculating unit 56, the summation for the mark by song each under same style is defined as the mark of this style;
Style determining unit 57 interested, exceedes the style of Second Threshold for filtering out style mark, be defined as user's style interested;
Second push unit 58, for having the special edition of preset incidence relation with user's style interested and sing and be singly pushed to user.
Optionally, Figure 10 illustrates another alternate configurations of the application's music data pusher, composition graphs 5 and Figure 10 known, music data pusher can also comprise:
Singer's taxon 59, for the singer of song for class condition, each song that user listens is classified;
Singer's score calculating unit 61, the summation for the mark by song each under same singer is defined as the mark of this singer;
Singer's determining unit 62 interested, for filtering out the singer of singer's mark more than the 3rd threshold value, is defined as user singer interested;
3rd push unit 63, for having the special edition of preset incidence relation with user singer interested and sing and be singly pushed to user.
The music data pusher that the embodiment of the present application provides, song pipelined data is listened by obtaining user's history, and calculate the mark of each song that user listens accordingly, wherein song score is directly proportional to the broadcasting time of song, be inversely proportional to the length of the play time distance current point in time of song, filter out the song that song score exceedes first threshold, as user's song interested, and will with user's song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.The application's device listens song flowing water to draw user's song interested in conjunction with user's history, and then pushes the special edition that associate with this song interested to user and sing singly, makes propelling movement result be more prone to the true hobby of being close to the users, improves user experience.
The embodiment of the present application also provides a kind of server, and this server can comprise music data pusher described above, and the description for music data pusher can refer to corresponding part above and describes, and repeats no more herein.
Be described the hardware configuration of the server that the embodiment of the present application provides below, the part relating to O&M audit in hereafter describing can refer to corresponding part above and describes.The hardware configuration schematic diagram of the server that Figure 11 provides for the embodiment of the present application, with reference to Figure 11, this server can comprise:
Processor 1, communication interface 2, storer 3, communication bus 4, and display screen 5;
Wherein processor 1, communication interface 2, storer 3 complete mutual communicating with display screen 5 by communication bus 4;
Optionally, communication interface 2 can be the interface of communication module, as the interface of gsm module;
Processor 1, for executive routine;
Storer 3, for depositing program;
Program can comprise program code, and described program code comprises the operational order of processor.
Processor 1 may be a central processor CPU, or specific integrated circuit ASIC (Application Specific Integrated Circuit), or is configured to the one or more integrated circuit implementing the embodiment of the present application.
Storer 3 may comprise high-speed RAM storer, still may comprise nonvolatile memory (non-volatile memory), such as at least one magnetic disk memory.
Wherein, program can be specifically for:
Obtain the history of user and listen song pipelined data, described history listens song pipelined data to comprise listened to the music song information, the broadcasting time of each song and play time;
Listen song pipelined data described in foundation, calculate the mark of each song that user listens, wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song;
Filter out the song that song score exceedes first threshold, be defined as user's song interested;
To with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
Finally, also it should be noted that, in this article, the such as relational terms of first and second grades and so on is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply the relation that there is any this reality between these entities or operation or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar portion mutually see.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the application.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein when not departing from the spirit or scope of the application, can realize in other embodiments.Therefore, the application can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (12)
1. a music data method for pushing, is characterized in that, comprising:
Obtain the history of user and listen song pipelined data, described history listens song pipelined data to comprise listened to the music song information, the broadcasting time of each song and play time;
Listen song pipelined data described in foundation, calculate the mark of each song that user listens, wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song;
Filter out the song that song score exceedes first threshold, be defined as user's song interested;
To with described user song interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
2. method according to claim 1, is characterized in that, listens song pipelined data described in described foundation, calculates the mark of each song that user listens, comprising:
In units of the time period of presetting, listen song pipelined data to divide to described history, the history obtaining some time section listens song pipelined data;
History for each time period listens song pipelined data to carry out the calculating of song score, and the score of same song in each time period is added, and obtains the gross score of each song.
3. method according to claim 2, is characterized in that, the described history for each time period listens song pipelined data to carry out the calculating of song score, and the score of same song in each time period is added, and obtains the gross score of each song, comprising:
History for each time period listens song pipelined data, calculates the score of each song according to the following equation:
Score
ti＝playcount
ti*e
-λt
Wherein, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date;
Calculate same song in the score of each time period and value:
4. method according to claim 2, it is characterized in that, described history listens song pipelined data also to comprise collecting state and the collection time point of each song, the described history for each time period listens song pipelined data to carry out the calculating of song score, and the score of same song in each time period is added, obtain the gross score of each song, comprising:
History for each time period listens song pipelined data, calculates the score of each song according to the following equation:
Score
ti＝(playcount
ti+kβ)*e
-λt
Wherein, when the i-th song k value when t time period is collected by user before current date is 1, when the i-th song k value when t time period is not collected by user before current date is 0, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date;
Calculate same song in the score of each time period and value:
5. method according to claim 1, is characterized in that, also comprises:
With style belonging to song for class condition, each song that user listens is classified;
The summation of the mark of song each under same style is defined as the mark of this style;
Filter out the style that style mark exceedes Second Threshold, be defined as user's style interested;
To with user's style interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
6. method according to claim 1, is characterized in that, also comprises:
With the singer of song for class condition, each song that user listens is classified;
The summation of the mark of song each under same singer is defined as the mark of this singer;
Filter out the singer of singer's mark more than the 3rd threshold value, be defined as user singer interested;
To with user singer interested, there is the special edition of preset incidence relation and sing and be singly pushed to user.
7. a music data pusher, is characterized in that, comprising:
Data capture unit, the history for obtaining user listens song pipelined data, and described history listens song pipelined data to comprise listened to the music song information, the broadcasting time of each song and play time;
Song score computing unit, for listening song pipelined data described in foundation, calculate the mark of each song that user listens, wherein the mark of song is directly proportional to the broadcasting time of song, is inversely proportional to the length of the play time distance current point in time of song;
Song determining unit interested, exceedes the song of first threshold for filtering out song score, be defined as user's song interested;
First push unit, for having the special edition of preset incidence relation with described user song interested and sing and be singly pushed to user.
8. device according to claim 7, is characterized in that, described song score computing unit comprises:
Data dividing unit, in units of the time period of presetting, listen song pipelined data to divide to described history, the history obtaining some time section listens song pipelined data;
Song score addition unit, for listening for the history of each time period song pipelined data to carry out the calculating of song score, and being added the score of same song in each time period, obtaining the gross score of each song.
9. device according to claim 8, is characterized in that, described song score addition unit comprises:
First computing unit, for listening song pipelined data for the history of each time period, calculates the score of each song according to the following equation:
Score
ti＝playcount
ti*e
-λt
Wherein, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date;
Second computing unit, for calculating same song in the score of each time period and value:
10. device according to claim 8, is characterized in that, described history listens song pipelined data also to comprise collecting state and the collection time point of each song, and described song score addition unit comprises:
3rd computing unit, for listening song pipelined data for the history of each time period, calculates the score of each song according to the following equation:
Score
ti＝(playcount
ti+kβ)*e
-λt
Wherein, when the i-th song k value when t time period is collected by user before current date is 1, when the i-th song k value when t time period is not collected by user before current date is 0, Score
tirepresent the score of the i-th song in t the time period before current date, playcount
tirepresent the broadcasting time of the i-th song in t the time period before current date;
4th computing unit, for calculating same song in the score of each time period and value:
11. devices according to claim 7, is characterized in that, also comprise:
Genre classification unit, for style belonging to song for class condition, each song that user listens is classified;
Style score calculating unit, the summation for the mark by song each under same style is defined as the mark of this style;
Style determining unit interested, exceedes the style of Second Threshold for filtering out style mark, be defined as user's style interested;
Second push unit, for having the special edition of preset incidence relation with user's style interested and sing and be singly pushed to user.
12. devices according to claim 7, is characterized in that, also comprise:
Singer's taxon, for the singer of song for class condition, each song that user listens is classified;
Singer's score calculating unit, the summation for the mark by song each under same singer is defined as the mark of this singer;
Singer's determining unit interested, for filtering out the singer of singer's mark more than the 3rd threshold value, is defined as user singer interested;
3rd push unit, for having the special edition of preset incidence relation with user singer interested and sing and be singly pushed to user.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510312808.8A CN104991900A (en) | 2015-06-09 | 2015-06-09 | Method and apparatus for pushing music data |
| TW105106706A TWI557669B (en) | 2015-06-09 | 2016-03-04 | Method and device for pushing music data |
| PCT/CN2016/081517 WO2016197774A1 (en) | 2015-06-09 | 2016-05-10 | Multimedia data pushing method and apparatus, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201510312808.8A CN104991900A (en) | 2015-06-09 | 2015-06-09 | Method and apparatus for pushing music data |
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| Publication Number | Publication Date |
|---|---|
| CN104991900A true CN104991900A (en) | 2015-10-21 |
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| Country | Link |
|---|---|
| CN (1) | CN104991900A (en) |
| TW (1) | TWI557669B (en) |
| WO (1) | WO2016197774A1 (en) |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105550243A (en) * | 2015-12-07 | 2016-05-04 | 广东欧珀移动通信有限公司 | Playlist processing method and device |
| CN105550272A (en) * | 2015-12-09 | 2016-05-04 | 小米科技有限责任公司 | Song recommending method and device |
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Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108055349B (en) * | 2017-12-28 | 2020-11-13 | 广州酷狗计算机科技有限公司 | Method, device and system for recommending K song audio |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090013002A1 (en) * | 2007-03-31 | 2009-01-08 | Sony Deutschland Gmbh | Method for content recommendation |
| CN101441667A (en) * | 2008-12-29 | 2009-05-27 | 北京搜狗科技发展有限公司 | Music recommend method and apparatus |
| CN102024058A (en) * | 2010-12-31 | 2011-04-20 | 万音达有限公司 | Music recommendation method and 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 |
| CN103218438A (en) * | 2013-04-18 | 2013-07-24 | 广东欧珀移动通信有限公司 | Method of recommending online music based on playing record of mobile terminal and mobile terminal |
| CN103678388A (en) * | 2012-09-19 | 2014-03-26 | 腾讯科技（深圳）有限公司 | Online music recommendation method and device |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060083119A1 (en) * | 2004-10-20 | 2006-04-20 | Hayes Thomas J | Scalable system and method for predicting hit music preferences for an individual |
| KR100893129B1 (en) * | 2007-10-24 | 2009-04-15 | 엔에이치엔(주) | Recommended Keyword Extraction System and Method for Multimedia Contents |
| WO2010027034A1 (en) * | 2008-09-05 | 2010-03-11 | 株式会社ソニー・コンピュータエンタテインメント | Content recommendation system, content recommendation method, content recommendation device, program, and information storage medium |
| TWI410811B (en) * | 2009-09-08 | 2013-10-01 | Univ Nat Cheng Kung | Music recommendation method and program product thereof |
| US20120023403A1 (en) * | 2010-07-21 | 2012-01-26 | Tilman Herberger | System and method for dynamic generation of individualized playlists according to user selection of musical features |
| US8671068B2 (en) * | 2011-09-22 | 2014-03-11 | Toyota Jidosha Kabushiki Kaisha | Content recommendation system |
| US20140156463A1 (en) * | 2012-11-05 | 2014-06-05 | Michael Benjamin HUI | Systems and methods for providing music |
| CN104123325B (en) * | 2013-04-28 | 2018-08-17 | 北京音之邦文化科技有限公司 | The recommendation method and recommendation server of multimedia file |
| CN104077339B (en) * | 2013-07-09 | 2016-05-11 | 腾讯科技（深圳）有限公司 | The methods, devices and systems that the user preference degree of multi-medium data obtains |
| CN104991900A (en) * | 2015-06-09 | 2015-10-21 | 腾讯科技（深圳）有限公司 | Method and apparatus for pushing music data |
-
2015
- 2015-06-09 CN CN201510312808.8A patent/CN104991900A/en active Pending
-
2016
- 2016-03-04 TW TW105106706A patent/TWI557669B/en active
- 2016-05-10 WO PCT/CN2016/081517 patent/WO2016197774A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090013002A1 (en) * | 2007-03-31 | 2009-01-08 | Sony Deutschland Gmbh | Method for content recommendation |
| CN101441667A (en) * | 2008-12-29 | 2009-05-27 | 北京搜狗科技发展有限公司 | Music recommend method and apparatus |
| CN102024058A (en) * | 2010-12-31 | 2011-04-20 | 万音达有限公司 | Music recommendation method and 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 |
| CN103678388A (en) * | 2012-09-19 | 2014-03-26 | 腾讯科技（深圳）有限公司 | Online music recommendation method and device |
| CN103218438A (en) * | 2013-04-18 | 2013-07-24 | 广东欧珀移动通信有限公司 | Method of recommending online music based on playing record of mobile terminal and mobile terminal |
Cited By (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016197774A1 (en) * | 2015-06-09 | 2016-12-15 | 腾讯科技（深圳）有限公司 | Multimedia data pushing method and apparatus, and storage medium |
| CN105550243A (en) * | 2015-12-07 | 2016-05-04 | 广东欧珀移动通信有限公司 | Playlist processing method and device |
| CN105550272A (en) * | 2015-12-09 | 2016-05-04 | 小米科技有限责任公司 | Song recommending method and device |
| WO2017124408A1 (en) * | 2016-01-21 | 2017-07-27 | 谢文 | Method for pushing information during automatic song replacement on mobile phone, and song replacement system |
| CN108021568A (en) * | 2016-10-31 | 2018-05-11 | 北京酷我科技有限公司 | One kind song is single to recommend method and device |
| CN108021568B (en) * | 2016-10-31 | 2020-09-08 | 北京酷我科技有限公司 | Singing bill recommendation method and device |
| CN106649645A (en) * | 2016-12-09 | 2017-05-10 | 腾讯音乐娱乐（深圳）有限公司 | Playlist processing method and device |
| CN108614840A (en) * | 2016-12-13 | 2018-10-02 | 北京酷智科技有限公司 | A kind of sort method and system of KTV songs |
| CN108614840B (en) * | 2016-12-13 | 2021-11-26 | 北京酷智科技有限公司 | KTV song sorting method and system |
| CN108255840A (en) * | 2016-12-28 | 2018-07-06 | 北京酷我科技有限公司 | A kind of recommendation method and system of song |
| CN106815352B (en) * | 2017-01-17 | 2018-09-04 | 维沃移动通信有限公司 | A kind of data push method and mobile terminal |
| CN106815352A (en) * | 2017-01-17 | 2017-06-09 | 维沃移动通信有限公司 | A kind of data push method and mobile terminal |
| CN107016062B (en) * | 2017-03-16 | 2021-02-26 | 中国联合网络通信有限公司广东省分公司 | Method and system for identifying trial listening cheating behaviors |
| CN107016062A (en) * | 2017-03-16 | 2017-08-04 | 中国联合网络通信有限公司广东省分公司 | A kind of audition cheating recognition methods and system |
| CN107463646A (en) * | 2017-07-24 | 2017-12-12 | 上海斐讯数据通信技术有限公司 | A kind of sleeping music intelligent recommendation method and device |
| CN107862085A (en) * | 2017-11-30 | 2018-03-30 | 郑州云海信息技术有限公司 | A kind of music supplying system based on big data |
| CN109446350A (en) * | 2018-11-09 | 2019-03-08 | 腾讯音乐娱乐科技（深圳）有限公司 | Multi-medium play method, device, terminal and storage medium |
| CN109885722B (en) * | 2019-01-07 | 2023-07-04 | 平安科技（深圳）有限公司 | Music recommendation method and device based on natural language processing and computer equipment |
| CN109885722A (en) * | 2019-01-07 | 2019-06-14 | 平安科技（深圳）有限公司 | Music recommended method, device and computer equipment based on natural language processing |
| CN111460207A (en) * | 2019-01-22 | 2020-07-28 | 上海博泰悦臻网络技术服务有限公司 | Vehicle and playing content recommendation method and device applied to vehicle |
| CN109982155B (en) * | 2019-03-25 | 2021-10-12 | 北京奇艺世纪科技有限公司 | Playlist recommendation method and system |
| CN109982155A (en) * | 2019-03-25 | 2019-07-05 | 北京奇艺世纪科技有限公司 | One kind broadcasting single recommended method and system |
| CN110837599B (en) * | 2019-11-14 | 2024-05-24 | 广州酷狗计算机科技有限公司 | Method, device, equipment and storage medium for pushing audio data |
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| CN111078931A (en) * | 2019-12-10 | 2020-04-28 | 腾讯科技（深圳）有限公司 | Singing sheet pushing method and device, computer equipment and storage medium |
| CN111078931B (en) * | 2019-12-10 | 2023-08-01 | 腾讯科技（深圳）有限公司 | Song list pushing method, device, computer equipment and storage medium |
| CN110990621B (en) * | 2019-12-16 | 2023-10-13 | 腾讯科技（深圳）有限公司 | Song recommendation method and device |
| CN110990621A (en) * | 2019-12-16 | 2020-04-10 | 腾讯科技（深圳）有限公司 | Song recommendation method and device |
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| CN115455229A (en) * | 2022-09-19 | 2022-12-09 | 北京字跳网络技术有限公司 | Song list recommending method, song list recommending device, song list recommending apparatus, song list recommending storage medium and program product |
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Also Published As
| Publication number | Publication date |
|---|---|
| TWI557669B (en) | 2016-11-11 |
| TW201643794A (en) | 2016-12-16 |
| WO2016197774A1 (en) | 2016-12-15 |
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