CN111046229A - Information pushing method and server side equipment - Google Patents
Information pushing method and server side equipment Download PDFInfo
- Publication number
- CN111046229A CN111046229A CN201811185202.2A CN201811185202A CN111046229A CN 111046229 A CN111046229 A CN 111046229A CN 201811185202 A CN201811185202 A CN 201811185202A CN 111046229 A CN111046229 A CN 111046229A
- Authority
- CN
- China
- Prior art keywords
- film
- user
- pushed
- movie
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
Abstract
The application provides an information pushing method and server-side equipment, wherein the method comprises the following steps: acquiring historical movie purchasing behavior data of a target user; acquiring the similarity between the film to be pushed and the purchased film; and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity. In the above example, based on similarity between films and behavior data of a user purchasing a film historically, the interest degree of the user to the film to be pushed can be determined, so that accurate film pushing can be performed on a target user, the technical problem that the existing film pushing efficiency is low is solved, and the technical effect of effectively improving the pushing efficiency is achieved.
Description
Technical Field
The application belongs to the technical field of internet, and particularly relates to an information pushing method and server side equipment.
Background
With the development of internet technology, mobile phone shopping and the like become more and more common, and not only can buy living goods, air tickets and the like on the internet, but also can buy movie tickets and the like on the internet. And, as people's standard of living increases, more and more people choose to go to a movie theater to watch a movie.
At present, in movie ticket selling software, the pushing sequence of the movie is generally set manually according to the cooperation condition of the software and the film side. However, the conversion rate of the push mode is low.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application aims to provide an information pushing method and server-side equipment so as to solve the problems of low accuracy and low efficiency of the existing information pushing.
The application provides an information pushing method and server-side equipment, which are realized as follows:
an information pushing method, the method comprising:
acquiring historical movie purchasing behavior data of a target user;
acquiring the similarity between the film to be pushed and the purchased film;
and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity.
An information pushing method, the method comprising:
acquiring purchased data of a target film;
acquiring historical movie purchasing behavior data of each user in a target push user group;
determining the interest degree of each user in the target film according to the purchased data and the historical movie purchasing behavior data of each user;
determining a crowd to be pushed from the target pushing user group according to the interest degree of each user in the target film;
and pushing the target film to the crowd to be pushed.
An information push method, comprising:
determining a target user;
determining an associated user having an association relation with the target user;
acquiring historical movie purchasing behavior data of the associated user;
acquiring the similarity between the film to be pushed and the purchased film;
and determining the film pushed to the target user according to the historical purchased film behavior data and the similarity.
An information presentation method, the method comprising:
determining the exhibition position of the display interface of the target user:
acquiring movie information to be displayed, wherein the movie information to be displayed is determined according to historical movie purchasing behavior data of the target user and the similarity between the movie to be pushed and the purchased movie;
and displaying the film information to be displayed on the exhibition booth.
A server device comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor performing the steps of:
acquiring historical movie purchasing behavior data of a target user;
acquiring the similarity between the film to be pushed and the purchased film;
and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity.
A server device comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor performing the steps of:
acquiring purchased data of a target film;
acquiring historical movie purchasing behavior data of each user in a target push user group;
determining the interest degree of each user in the target film according to the purchased data and the historical movie purchasing behavior data of each user;
determining a crowd to be pushed from the target pushing user group according to the interest degree of each user in the target film;
and pushing the target film to the crowd to be pushed.
A computer readable storage medium having stored thereon computer instructions that when executed perform the steps of: acquiring historical movie purchasing behavior data of a target user;
acquiring the similarity between the film to be pushed and the purchased film;
and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity.
A computer readable storage medium having stored thereon computer instructions that when executed perform the steps of:
acquiring purchased data of a target film;
acquiring historical movie purchasing behavior data of each user in a target push user group;
determining the interest degree of each user in the target film according to the purchased data and the historical movie purchasing behavior data of each user;
determining a crowd to be pushed from the target pushing user group according to the interest degree of each user in the target film;
and pushing the target film to the crowd to be pushed.
The information pushing method and the server side equipment provided by the application acquire historical movie purchasing behavior data of a target user; acquiring the similarity between the film to be pushed and the purchased film; and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity. Namely, based on similarity between films and behavior data of historical film purchase of a user, the interest degree of the user to the film to be pushed can be determined, so that accurate film pushing can be performed on a target user, the technical problem that the existing film pushing efficiency is low is solved, and the technical effect of effectively improving the pushing efficiency is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is an architecture diagram of an information push system provided herein;
FIG. 2 is a diagram illustrating the results of a recommendation made by a movie provided herein;
FIG. 3 is a schematic diagram of the results of recommendations made by a user provided herein;
FIG. 4 is a schematic illustration of an interface for movie recommendation at a payment success interface provided herein;
FIG. 5 is a schematic diagram of an interface for recommending a movie on a bread stick according to the present application;
FIG. 6 is a schematic diagram of an interface for recommending movies on a standby interface according to the present application;
FIG. 7 is a schematic diagram of a recommendation result analysis based on the recommendation method of this example provided in the present application;
fig. 8 is a flowchart of a method of an information push method provided in the present application;
FIG. 9 is a flow chart of another method of the information push method provided herein;
FIG. 10 is a schematic diagram of a server-side architecture provided herein;
FIG. 11 is a block diagram of an information pushing apparatus provided in the present application;
fig. 12 is a block diagram of an information push apparatus according to the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Considering the existing ticket purchasing software, the pushing of the film is generally determined based on the cooperation situation of the software and the film side, that is, the pushing sequence and the like are only based on the cooperation situation of the film side and the software, and do not relate to the commonality between the films and the historical behavior preference of the user, and the conversion rate of the pushing mode is low. Therefore, in this example, if recommendation of movies can be performed by combining commonalities among movies and historical behavior preferences of users, conversion efficiency of movie pushing can be effectively improved, and pushing results can be more scientific and effective.
Specifically, the present application provides an information push system, as shown in fig. 1, which may include: the system comprises a server 101 and a user terminal 102, wherein the server 101 pushes objects to the user terminal 102 according to the commonality between target objects and the historical behavior preference of the user.
The user terminal may be a terminal device or software used by a user. Specifically, the user side may be a terminal device such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart watch, or other wearable devices. Of course, the user terminal may also be software that can run in the terminal device. For example: and the mobile phone carries out treasure panning, ticket panning or application software such as a browser and the like.
The server may be a single server or a server cluster. The server has data processing capacity and can be connected with a database, historical movie purchasing behavior data of each user, purchased data of movies to be pushed and the like are stored in the database, and effective pushing of the movies can be achieved based on the data.
In a specific implementation, the server may intelligently deliver the resource, for example, the preference level of the user for each purchased movie may be detected according to the RFM preference of the user for each purchased movie (i.e., the real user behavior of the user for 3 dimensions of time, frequency and payment amount of purchasing each movie). Then, similarity among the films is obtained based on collaborative filtering of the films, and reasonable recommendation is conducted. In the specific recommendation process, users can be circled according to the movies, and a single target user can be pushed according to the preference value of each movie of the users. In this way, because the video is based on the user preference and combined with the similarity between videos, a suitable video can be pushed for the user, and the user experience is improved.
The determination of the similarity between the movies may be based on the number of users who purchased both movie a and movie B. This is because a user who purchased movie a and movie B can indicate the possibility that movie a and movie B are liked by the same user.
For example, when implemented, the similarity between movies may be calculated based on the following formula:
where | n (i) | represents the number of users who purchased movie i, | n (j) | represents the number of users who purchased movie j, and | n (i) ∩ n (j) | represents the number of users who purchased movie i and movie j at the same time.
The formulation of the similarity between two movies based on this formula is based on the fact that two movies are liked by many users in common, and the more people liked in common, indicates that the similarity between two movies is higher. For example, if the number of users purchasing movie i is 100, the number of users purchasing movie j is 400, and the number of users purchasing both movie i and movie j is 50, then the similarity may be: 50/200 ═ 0.25.
However, it should be noted that the above formula for finding the similarity between films is only an exemplary formula, and other formulas may be used to find the similarity between films in practical implementation. For example, it may be determined based on the following formula:
the specific formula used as the formula for calculating the similarity between films can be determined according to actual needs, and the formula is not limited in the application.
Specifically, assuming that the movie j is a movie to be pushed, i.e. a movie to be shown, then it may be possibleAcquiring the pre-sale data of the film to determine the similarity between the film j and the film purchased by the user, namely acquiring the number of users purchasing the film j based on the pre-sale data of the film j, and simultaneously purchasing the number of users of the film i and the film j, thereby determining the similarity W between the film i and the film jij. Wherein, in the case that the movie j is a movie to be pushed, the similarity W between the movie i and the movie j is based onijThe interest level of the user in movie j can be determined.
When the user behavior preference is determined, the user may purchase a movie in a historical purchase order, and the difference (R) between the purchase time and the showing time of the movie, the number of purchases (F), the single ticket price (M), and the like are weighted to calculate the interest value of the user in each movie.
Wherein, the purchase time and the movie showing time difference (R) may be: pay time-mapping time. In conjunction with the time characteristics of the service, it is known that a ticket can be purchased in advance for a movie that is about to be shown, and if the user purchases a ticket before showing, the earlier the user purchases, indicating that the user has a greater interest in the movie.
In specific implementation, the method can be divided into a plurality of levels according to the difference between the purchase time and the showing time of the user, and a corresponding weight value is set for each level. For example, the time difference may be divided into 5 levels, level 1 (20 days ahead and more, weight value of 20; 15 days to 19 days ahead, weight value of 15; 10 days to 14 days ahead, weight value of 10; 5 days to 9 days ahead, weight value of 5; 0 days to 4 days ahead, weight value of 2). Based on the user ticket buying time and the showing time, the R value of the user's likeness degree of purchasing the film based on the purchasing time can be obtained.
However, it should be noted that the specific time and weight value listed above are only an exemplary description, and other time and weight values may be adopted in practical implementation, and this application is not limited to this, and may be selected according to practical needs.
Wherein, the number of purchases (F) may be: the number of purchases of a single movie by a single user is analyzed. Specifically, the number of ticket purchases can be divided into n files according to the distribution of the number of purchase times of the movie by the user. For example: different weight values may be assigned based on the number of times the user purchased the ticket. For example, the number of times of ticket purchase is 1, the weight value is 5, the number of times of ticket purchase is 2, the weight value is 20, the number of times of ticket purchase is 3, the weight value is 30, and the like. However, in real life, the possibility of ticket swiping may exist when ticket buying times are too many, or the possibility of purchasing the ticket instead is difficult to guarantee that the ticket is watched by the party or the ticket is liked by the party. Therefore, the ticket purchase times can be limited to be more than the preset times (for example, 10 times), and the weight value can be returned to a normal level, for example, if the ticket purchase times exceed 10 times, the weight value is set to 10, and so on. Based on the number of times of ticket purchase by the user, an F value can be obtained in which the user's likeness to purchase the movie is based on the number of times of purchase.
However, it should be noted that the above listed specific matching method and specific weight value based on matching the corresponding levels according to the number of times of ticket purchase, and the weight value corresponding to each level are only an exemplary description, and when the matching method and the weight value are actually implemented, the matching method and the weight value may be set according to actual requirements, which is not limited in the present application.
Wherein the single fare (M) may be: the sum and the times of single movie purchase of a single user are averaged, and the weight is distributed according to the average price of movie tickets purchased by the user. For example: the amounts are ranked into n levels according to the calculated average amount distribution, wherein the levels may be normally distributed. For example, a corresponding weight value of 3 is set below the fare of 20, a corresponding weight value of 8 is set between 20 and 30, a corresponding weight value of 30 to 40 is set, a corresponding weight value of 13 is set, a fare of 40 to 60 is set, a corresponding weight value of 20 is set, a fare of 60 to 80 is set, a corresponding weight value of 30 is set, a fare of 80 is set, a corresponding weight value of 50 is set, and the like. That is, based on the ticket price at which the user purchased the ticket, the F value based on the purchase price can be obtained as the user's likeness to purchase the movie.
At the time of implementation, there is sometimes some dirty data, such as: data which are successful in transaction but empty in money can be removed, or an extremely low weight value is set, so that the data are prevented from influencing a real result.
However, it should be noted that the above listed specific matching method and specific weight value based on matching the corresponding levels of the purchase price and the weight value corresponding to each level are only an exemplary description, and when the matching method and the weight value are actually implemented, the matching method and the weight value may be set according to actual requirements, which is not limited in the present application.
In actual implementation, after the similarity between the movies and the interest degree of the user in the purchased movie are obtained, the interest of the user i in one movie j can be calculated according to the following formula:
wherein, PijShowing the interest degree of a user i in a movie j, N (i) showing a movie set (j is a movie which the user likes), S (x, y) showing y movie sets (x is a movie in the set) which are most similar to the movie x, and WjxRepresenting the similarity of film j and film x, RixIndicating the interest level of the user i in the movie x.
That is, the above formula is based on the similarity (W) between the purchased film and the film to be pushedjx) And the interest level (R) of the user in the purchased filmix) To determine the interest of the user i in the movie j to be pushed.
For example, according to the historical movie purchasing behavior data of the user a, it can be determined that the user purchased movie 1, movie 2, movie 3, movie 4, movie 5, and movie 6. Based on the historical movie purchasing behavior data (purchase times, purchase time difference, purchase price, etc.) of user a, the interestingness of each of user a, movie 1, movie 2, movie 3, movie 4, movie 5, and movie 6, can be determined. The film to be pushed is film 7 and film 8, based on the number of users in the big data group to film 1, film 2, film 3, film 4, the number of users purchasing film 5 and film 6 and the users purchasing film 7 and film 8 in the user group, and the number of users purchasing coincided, the similarity of film 7 with film 1, film 2, film 3, film 4, film 5 and film 6 respectively can be determined, and film 8 is with film 1, film 2, film 3, film 4, film 5 and film 6 respectively.
After determining the interest degrees of the user a in the films 1, 2, 3, 4, 5 and 6, the similarity between the film 7 and the films 1, 2, 3, 4, 5 and 6, and the similarity between the film 8 and the films 1, 2, 3, 4, 5 and 6, respectively, the interest degrees of the user a in the films 7 and the interest degrees of the user a in the films 8 can be calculated by using the above formulas.
Through the mode in the above example, the interest degree of a user in a certain film can be effectively determined, and the accurate pushing of the film can be realized based on the determined interest degree. Specifically, there may be two delivery scenarios:
1) according to the movie pushing, namely, one or more movies to be shown are released to a plurality of intended users;
as shown in fig. 2, a diagram of a result according to movie pushing is shown, where show _ id represents a movie identifier, show _ name represents a movie name, and the number of users represents the number of users to be finally pushed.
In the actual pushing, users with interest degrees of certain movies larger than a preset threshold may all be used as trigger users to push. For example, those with an interest level greater than 1% are used as trigger users. The number of users having an interest level of "puck hotel 3" of more than 1% is the largest, and the number of times the movie is pushed is the largest.
2) And performing multi-film advertisement delivery on the user according to user push, namely, according to the calculated sequence of the interestingness of the user to each film.
As shown in fig. 3, a diagram of the result according to the user push is shown, where user _ id represents the user identifier, show _ id represents the movie identifier, open _ day represents the showing time, com _ key represents the user interest level, and rank1 represents the like degree sorting.
In the actual pushing, a film with an interest degree exceeding a preset threshold value in the to-be-shown film of the user may be selected as the film to be pushed to the user, or a film with an interest degree located at a previous preset position in the to-be-shown film of the user may be selected as the film to be pushed to the user. The specific method can be selected according to actual needs, and the application does not limit the method.
In one embodiment, when the movie is pushed, the pushing of the movie information may be performed by, but not limited to, one of the following methods: a payment success interface, a starting interface, a standby interface, a bread bar interface and a popup interface.
Fig. 4 is a schematic diagram of performing movie pushing on a payment success interface, fig. 5 is a schematic diagram of performing movie pushing on a bread-loaf interface, and fig. 6 is a schematic diagram of performing movie pushing on a standby interface.
However, it should be noted that the schematic diagram and the manner of the movie pushing interface listed in the above example are only an exemplary description, and other movie pushing manners may be selected according to requirements in practical implementation, which is not limited in this application. Through the push modes, the film push conversion rate can be effectively improved, for example, the user is recommended to buy a film to be shown in the future on the payment success interface, and the re-purchase rate of the user can be improved. Specifically, the score of the user for the future movie can be calculated according to a recommendation algorithm, and different movies are recommended for different users. In specific implementation, a PUSH message PUSH mode may also be adopted, that is, as shown in fig. 6, this mode may control the PUSH frequency (for example, one PUSH is performed in one day) in combination with the APP usage by the user, and different pushes are performed according to different preferences of the user, so as to reduce the disturbance to the user and optimize the user experience. The specific push frequency and push mode can be selected according to actual needs, which is not limited in the present application.
In the above example, the algorithm model used for recommending the movie can be continuously learned and trained every day, and the preference of each user is tracked in real time to obtain the optimal recommendation ranking.
As shown in fig. 7, the hit rate of the demon 3 shown on day 8, 17 th day 8 can reach 87.5%, and the hit rate of the demon shown on day 8, 24 th day 8 can reach 65.6%, which is a test result chart of the movie shown on 8.8 recommended after 8.16 th day.
In this example, an information push method is also provided, which pushes a movie for a target user, as shown in fig. 8, and may include the following steps:
step 801: acquiring historical movie purchasing behavior data of a target user;
wherein the historical purchase movie behavior data may include, but is not limited to, at least one of: time difference between purchase time and show time, number of purchases, and purchase price.
Step 802: acquiring the similarity between the film to be pushed and the purchased film;
specifically, obtaining the similarity between the movie to be pushed and the purchased movie may include:
s1: acquiring the number of purchasing users of a film to be pushed;
s2: acquiring the number of purchasing users of the purchased film;
s3: acquiring the number of users who purchase the films to be pushed and the purchased films;
s4: determining the similarity between the film to be pushed and the purchased film according to the number of users purchasing the film to be pushed, the number of users purchasing the film and the number of users purchasing the film to be pushed and the film.
Step 803: and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity.
In the above step 803, determining whether to push the movie to be pushed to the target user according to the historical movie purchasing behavior data and the similarity may include: determining the interest degree of the target user in the purchased film according to the historical movie purchasing behavior data; determining the interest degree of the target user in the to-be-pushed film according to the interest degree of the purchased film and the similarity; and determining whether to push the film to be pushed to the target user according to the interest degree of the target user in the film to be pushed. That is, it may be determined whether to push, and which to push, based on the interestingness.
For example, determining whether to push the movie to be pushed to the target user according to the interest level of the target user in the movie to be pushed may include: under the condition that the interest degree of the target user on the film to be pushed is higher than a preset threshold value, pushing the film to be pushed to the target user; or, the film of which the interest degree of the target user is located at a preset position in the film to be pushed is used as the film to be pushed to the target user.
When specifically performing movie pushing, in a case that it is determined that the movie to be pushed is pushed to the target user, the movie to be pushed may be pushed to the target user by at least one of, but not limited to: a starting-up interface, a placing success interface, a bread bar interface and a popup interface.
As shown in fig. 9, an information push method is further provided, which is based on movie push, and may include the following steps:
step 901: acquiring purchased data of a target film;
step 902: acquiring historical movie purchasing behavior data of each user in a target push user group;
step 903: determining the interest degree of each user in the target film according to the purchased data and the historical movie purchasing behavior data of each user;
specifically, determining the interest level of each user in the target movie according to the purchased data and the historical movie purchasing behavior data of each user may include:
s1: determining the interest degree of each user in each purchased film according to the historical film purchasing behavior data of each user;
s2: determining the similarity between each purchased film and the target film according to the purchased data;
wherein, according to the purchased data, determining the similarity between each purchased film and the target film may include: acquiring the number of purchasing users of the target film according to the purchased data; acquiring the number of purchasing users of each purchased film; acquiring the number of users who purchase the films to be pushed and the purchased films; and determining the similarity between the film to be pushed and each purchased film according to the number of users purchasing the film to be pushed, the number of users purchasing the film and the number of users purchasing the film to be pushed and the film.
S3: and determining the interest degree of each user in the target film according to the interest degree of each user in the purchased film and the similarity between each purchased film and the target film.
Step 904: determining a crowd to be pushed from the target pushing user group according to the interest degree of each user in the target film;
step 905: and pushing the target film to the crowd to be pushed.
When a group of people to be pushed is determined from the target pushing user group according to the interest degree of each user in the target film, the users with the interest degree exceeding a preset threshold value in the target pushing user group can be used as the group of people to be pushed.
The historical purchase film behavior data may include, but is not limited to, at least one of: time difference between purchase time and show time, number of purchases, and purchase price.
When the movie pushing is specifically performed, the target movie may be pushed to the crowd to be pushed by at least one of the following manners: a starting-up interface, a placing success interface, a bread bar interface and a popup interface.
Further, when a crowd to be pushed is determined from the target pushing user group according to the interest degree of each user in the target movie, the associated user of the user with the interest degree exceeding a preset threshold value in the target pushing user group can be used as the crowd to be pushed, wherein the associated user can be but is not limited to include: and the users with the social property or the family property with the users with the interestingness exceeding the preset threshold value. That is, when it is determined that some users are interested in the movie, the movie can be pushed to his family or his friends, so as to achieve a wider popularization effect.
In the application, an information pushing method is also provided, which may include the following steps:
step 1: determining a target user;
step 2: determining an associated user having an association relationship with the target user, wherein the determining of the associated user having the association relationship with the target user may be to use a user having a social attribute or a family attribute with the target user as the associated user;
and step 3: acquiring historical movie purchasing behavior data of the associated user;
and 4, step 4: acquiring the similarity between the film to be pushed and the purchased film;
and 5: and determining the film pushed to the target user according to the historical purchased film behavior data and the similarity.
That is, in the above example, the movie pushed to the target user is determined based on the ticket purchasing behavior of the associated user of the target user. For example, to push a movie to user 1, the movie pushed to user 1 may be determined from the historical ticketing behavior of the friend of user 1 (user 2), rather than or not only based on the historical ticketing behavior of user 1.
There is also provided, in accordance with an embodiment of the present invention, an embodiment of a method for push delivery of information, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
It should be noted that, in the above example, the description is given by taking the movie information as the information to be pushed, and in actual implementation, other objects to be pushed may be used, for example: newly shelved merchandise, concert tickets, etc. This is not a limitation of the present application.
The method provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar operation device. Taking the operation on the server as an example, fig. 10 is a hardware structure block diagram of a server device of an information push method according to an embodiment of the present invention. As shown in fig. 10, the server device 10 may include one or more processors 102 (only one is shown in the figure) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 10 is merely illustrative and is not intended to limit the structure of the electronic device. For example, the server device 10 may also include more or fewer components than shown in FIG. 10, or have a different configuration than shown in FIG. 10.
The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the information pushing method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the information pushing method of the application program. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission module 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In the software layer, the information pushing apparatus may be as shown in fig. 11, and includes: a first obtaining module 1101, a second obtaining module 1102 and a determining module 1103, wherein:
a first obtaining module 1101, configured to obtain historical movie purchasing behavior data of a target user;
a second obtaining module 1102, configured to obtain similarity between a movie to be pushed and a purchased movie;
a determining module 1103, configured to determine, according to the historical movie purchasing behavior data and the similarity, whether to push the movie to be pushed to the target user.
In one embodiment, the historical purchasing behavior data may include, but is not limited to, at least one of: time difference between purchase time and show time, number of purchases, and purchase price.
In an embodiment, the determining module 1103 may specifically determine, according to the historical movie purchasing behavior data, the interest level of the target user in purchased movies; determining the interest degree of the target user in the to-be-pushed film according to the interest degree of the purchased film and the similarity; and determining whether to push the film to be pushed to the target user according to the interest degree of the target user in the film to be pushed.
In an embodiment, determining whether to push the movie to be pushed to the target user according to the interest level of the target user in the movie to be pushed may include: under the condition that the interest degree of the target user on the film to be pushed is higher than a preset threshold value, pushing the film to be pushed to the target user; or, the film of which the interest degree of the target user is located at a preset position in the film to be pushed is used as the film to be pushed to the target user.
In one embodiment, in the case that it is determined to push the to-be-pushed movie to the target user, the to-be-pushed movie may be pushed to the target user by, but not limited to, at least one of the following manners: a starting-up interface, a placing success interface, a bread bar interface and a popup interface.
In one embodiment, the second obtaining module 1102 may specifically obtain the number of users purchasing a movie to be pushed; acquiring the number of purchasing users of the purchased film; acquiring the number of users who purchase the films to be pushed and the purchased films; determining the similarity between the film to be pushed and the purchased film according to the number of users purchasing the film to be pushed, the number of users purchasing the film and the number of users purchasing the film to be pushed and the film.
At the software level, there is also provided an information pushing apparatus, as shown in fig. 12, which may include: a first obtaining module 1201, a second obtaining module 1202, a first determining module 1203, a second determining module 1204, and a pushing module 1205, where:
a first obtaining module 1201, configured to obtain purchased data of a target movie;
a second obtaining module 1202, configured to obtain historical movie purchasing behavior data of each user in the target push user group;
a first determining module 1203, configured to determine, according to the purchased data and historical movie purchasing behavior data of each user, a degree of interest of each user in the target movie;
a second determining module 1204, configured to determine, according to the interest degree of each user in the target movie, a crowd to be pushed from the target pushing user group;
a pushing module 1205, configured to push the target movie to the crowd to be pushed.
In an embodiment, the second determining module 1204 may specifically determine, as the crowd to be pushed, a user with an interest degree exceeding a preset threshold in the target group of pushing users.
In one embodiment, the first determining module 1203 may specifically determine, according to historical movie purchasing behavior data of each user, a degree of interest of each user in each purchased movie; determining the similarity between each purchased film and the target film according to the purchased data; and determining the interest degree of each user in the target film according to the interest degree of each user in the purchased film and the similarity between each purchased film and the target film.
In one embodiment, determining the similarity between each purchased film and the target film according to the purchased data may include: acquiring the number of purchasing users of the target film according to the purchased data; acquiring the number of purchasing users of each purchased film; acquiring the number of users who purchase the films to be pushed and the purchased films; and determining the similarity between the film to be pushed and each purchased film according to the number of users purchasing the film to be pushed, the number of users purchasing the film and the number of users purchasing the film to be pushed and the film.
In one embodiment, the historical purchase movie behavior data may include, but is not limited to, at least one of: time difference between purchase time and show time, number of purchases, and purchase price.
In one embodiment, the target movie may be pushed to the crowd to be pushed by at least one of, but not limited to: a starting-up interface, a placing success interface, a bread bar interface and a popup interface.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
The apparatuses or modules illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. The functionality of the modules may be implemented in the same one or more software and/or hardware implementations of the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or sub-units in combination.
The methods, apparatus or modules described herein may be implemented in computer readable program code to a controller implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, Application Specific Integrated Circuits (ASICs), programmable logic controllers and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
Some of the modules in the apparatus described herein may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary hardware. Based on such understanding, the technical solutions of the present application may be embodied in the form of software products or in the implementation process of data migration, which essentially or partially contributes to the prior art. The computer software product may be stored in a storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
While the present application has been described with examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the spirit of the application.
Claims (20)
1. An information pushing method, characterized in that the method comprises:
acquiring historical movie purchasing behavior data of a target user;
acquiring the similarity between the film to be pushed and the purchased film;
and determining whether to push the film to be pushed to the target user according to the historical behavior data of the purchased film and the similarity.
2. The method of claim 1, wherein the historical purchase film behavior data comprises at least one of: time difference between purchase time and show time, number of purchases, and purchase price.
3. The method according to claim 1, wherein determining whether to push the movie to be pushed to the target user according to the historical movie purchasing behavior data and the similarity comprises:
determining the interest degree of the target user in the purchased film according to the historical movie purchasing behavior data;
determining the interest degree of the target user in the to-be-pushed film according to the interest degree of the purchased film and the similarity;
and determining whether to push the film to be pushed to the target user according to the interest degree of the target user in the film to be pushed.
4. The method according to claim 3, wherein determining whether to push the movie to be pushed to the target user according to the interest level of the target user in the movie to be pushed comprises:
under the condition that the interest degree of the target user on the film to be pushed is higher than a preset threshold value, pushing the film to be pushed to the target user;
alternatively, the first and second electrodes may be,
and taking the film of which the interest degree of the target user is located at a preset position in the film to be pushed as the film to be pushed to the target user.
5. The method according to claim 1, wherein in case that it is determined to push the movie to be pushed to the target user, the movie to be pushed is pushed to the target user by at least one of:
a starting-up interface, a placing success interface, a bread bar interface and a popup interface.
6. The method according to claim 1, wherein obtaining similarity between the movie to be pushed and the purchased movie comprises:
acquiring the number of purchasing users of a film to be pushed;
acquiring the number of purchasing users of the purchased film;
acquiring the number of users who purchase the films to be pushed and the purchased films;
determining the similarity between the film to be pushed and the purchased film according to the number of users purchasing the film to be pushed, the number of users purchasing the film and the number of users purchasing the film to be pushed and the film.
7. An information pushing method, characterized in that the method comprises:
acquiring purchased data of a target film;
acquiring historical movie purchasing behavior data of each user in a target push user group;
determining the interest degree of each user in the target film according to the purchased data and the historical movie purchasing behavior data of each user;
determining a crowd to be pushed from the target pushing user group according to the interest degree of each user in the target film;
and pushing the target film to the crowd to be pushed.
8. The method according to claim 7, wherein determining a group of people to be pushed from the target pushing user group according to the interest degree of each user in the target movie comprises:
and taking the users with the interest degrees exceeding a preset threshold value in the target pushing user group as the to-be-pushed user group.
9. The method according to claim 7, wherein determining a group of people to be pushed from the target pushing user group according to the interest degree of each user in the target movie comprises:
and taking the associated user of the user with the interest degree exceeding a preset threshold value in the target pushing user group as the group to be pushed, wherein the associated user comprises: and the users with the social attribute or the family attribute with the users with the interestingness exceeding the preset threshold value.
10. The method according to claim 7, wherein determining interest level of each user in the target movie according to the purchased data and historical purchase movie behavior data of each user comprises:
determining the interest degree of each user in each purchased film according to the historical film purchasing behavior data of each user;
determining the similarity between each purchased film and the target film according to the purchased data;
and determining the interest degree of each user in the target film according to the interest degree of each user in the purchased film and the similarity between each purchased film and the target film.
11. The method according to claim 10, wherein determining similarity of each purchased film to the target film based on the purchased data comprises:
acquiring the number of purchasing users of the target film according to the purchased data;
acquiring the number of purchasing users of each purchased film;
acquiring the number of users who purchase the films to be pushed and the purchased films;
and determining the similarity between the film to be pushed and each purchased film according to the number of users purchasing the film to be pushed, the number of users purchasing the film and the number of users purchasing the film to be pushed and the film.
12. The method of claim 7, wherein the historical purchase film behavior data comprises at least one of: time difference between purchase time and show time, number of purchases, and purchase price.
13. The method according to claim 7, wherein the target movie is pushed to the crowd to be pushed by at least one of:
a starting-up interface, a placing success interface, a bread bar interface and a popup interface.
14. An information pushing method, comprising:
determining a target user;
determining an associated user having an association relation with the target user;
acquiring historical movie purchasing behavior data of the associated user;
acquiring the similarity between the film to be pushed and the purchased film;
and determining the film pushed to the target user according to the historical purchased film behavior data and the similarity.
15. The method of claim 14, wherein determining the associated user with which the target user has an association relationship comprises:
and taking the user with social attributes or family attributes with the target user as the associated user.
16. An information presentation method, the method comprising:
determining the exhibition position of the display interface of the target user:
acquiring movie information to be displayed, wherein the movie information to be displayed is determined according to historical movie purchasing behavior data of the target user and the similarity between the movie to be pushed and the purchased movie;
and displaying the film information to be displayed on the exhibition booth.
17. A server device comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 6.
18. A server device comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 7 to 13.
19. A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 6.
20. A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 7 to 13.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811185202.2A CN111046229A (en) | 2018-10-11 | 2018-10-11 | Information pushing method and server side equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811185202.2A CN111046229A (en) | 2018-10-11 | 2018-10-11 | Information pushing method and server side equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111046229A true CN111046229A (en) | 2020-04-21 |
Family
ID=70229086
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811185202.2A Pending CN111046229A (en) | 2018-10-11 | 2018-10-11 | Information pushing method and server side equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111046229A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112819533A (en) * | 2021-01-29 | 2021-05-18 | 深圳脉腾科技有限公司 | Information pushing method and device, electronic equipment and storage medium |
| CN113269594A (en) * | 2021-06-08 | 2021-08-17 | 绍兴市壹点通传媒有限公司 | Cinema advertisement management method and system based on big data |
| CN113282825A (en) * | 2021-05-28 | 2021-08-20 | 青岛海尔科技有限公司 | Information pushing method and device, storage medium and electronic device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6266649B1 (en) * | 1998-09-18 | 2001-07-24 | Amazon.Com, Inc. | Collaborative recommendations using item-to-item similarity mappings |
| CN103744966A (en) * | 2014-01-07 | 2014-04-23 | Tcl集团股份有限公司 | Item recommendation method and device |
| CN104346476A (en) * | 2014-11-20 | 2015-02-11 | 西安电子科技大学 | Personalized object recommending method based on object similarity and network structure |
| WO2016125237A1 (en) * | 2015-02-02 | 2016-08-11 | 楽天株式会社 | Recommendation device, recommendation method, recording medium, and program |
| CN107277115A (en) * | 2017-05-27 | 2017-10-20 | 深圳大学 | A kind of content delivery method and device |
| CN107506459A (en) * | 2017-08-29 | 2017-12-22 | 环球智达科技（北京）有限公司 | A kind of film recommendation method based on film similarity |
| CN108230059A (en) * | 2016-12-13 | 2018-06-29 | 中国联合网络通信集团有限公司 | Film recommends method and device |
-
2018
- 2018-10-11 CN CN201811185202.2A patent/CN111046229A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6266649B1 (en) * | 1998-09-18 | 2001-07-24 | Amazon.Com, Inc. | Collaborative recommendations using item-to-item similarity mappings |
| CN103744966A (en) * | 2014-01-07 | 2014-04-23 | Tcl集团股份有限公司 | Item recommendation method and device |
| CN104346476A (en) * | 2014-11-20 | 2015-02-11 | 西安电子科技大学 | Personalized object recommending method based on object similarity and network structure |
| WO2016125237A1 (en) * | 2015-02-02 | 2016-08-11 | 楽天株式会社 | Recommendation device, recommendation method, recording medium, and program |
| CN108230059A (en) * | 2016-12-13 | 2018-06-29 | 中国联合网络通信集团有限公司 | Film recommends method and device |
| CN107277115A (en) * | 2017-05-27 | 2017-10-20 | 深圳大学 | A kind of content delivery method and device |
| CN107506459A (en) * | 2017-08-29 | 2017-12-22 | 环球智达科技（北京）有限公司 | A kind of film recommendation method based on film similarity |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112819533A (en) * | 2021-01-29 | 2021-05-18 | 深圳脉腾科技有限公司 | Information pushing method and device, electronic equipment and storage medium |
| CN113282825A (en) * | 2021-05-28 | 2021-08-20 | 青岛海尔科技有限公司 | Information pushing method and device, storage medium and electronic device |
| CN113269594A (en) * | 2021-06-08 | 2021-08-17 | 绍兴市壹点通传媒有限公司 | Cinema advertisement management method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10649818B2 (en) | Multi-touch attribution model for valuing impressions and other online activities | |
| US20190012683A1 (en) | Method for predicting purchase probability based on behavior sequence of user and apparatus for the same | |
| TWI393064B (en) | Device, method, system, and processor readable medium for event communication platform for mobile device users | |
| US20140279121A1 (en) | Customizable and adjustable pricing of games | |
| US20140274355A1 (en) | Dynamic recommendation of games | |
| CN107729443A (en) | Loan product promotion method, device and computer-readable recording medium | |
| US20140274354A1 (en) | Intelligent merchandising of games | |
| CN107808314B (en) | User recommendation method and device | |
| US20130091130A1 (en) | Systems and methods that utilize preference shields as data filters | |
| US20140006172A1 (en) | Method of calculating a reserve price for an auction and apparatus conducting the same | |
| CN111046229A (en) | Information pushing method and server side equipment | |
| US10621641B2 (en) | Method and device for pushing information | |
| WO2020043001A1 (en) | Advertisement placement method, method for determining popularization population, server, and terminal | |
| US20130311332A1 (en) | Customized marketing | |
| CN112035717A (en) | Information processing method, device, equipment and storage medium | |
| CN110210900A (en) | Method, apparatus and equipment are determined based on the reference product of transaction association | |
| CN111159553A (en) | Information pushing method and device, computer equipment and storage medium | |
| WO2020072307A1 (en) | Determining sellability score and cancellability score | |
| CN111626767A (en) | Resource data distribution method, device and equipment | |
| CN110796520A (en) | Commodity recommendation method and device, computing equipment and medium | |
| CN116127184A (en) | Product recommendation method and device, nonvolatile storage medium and electronic equipment | |
| CN111193598B (en) | Group chat session recommendation method and device | |
| CN113781171A (en) | Information pushing method, device, equipment and storage medium | |
| CN113506171A (en) | Bank package recommendation method and device | |
| CN109685561B (en) | Electronic certificate pushing method and device based on user behavior and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |