CN112015948B - Video recommendation method and device, electronic equipment and storage medium - Google Patents
Video recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/74—Browsing; Visualisation therefor
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
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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
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F3/04842—Selection of displayed objects or displayed text elements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F3/04847—Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
Abstract
The embodiment of the invention relates to a video recommendation method, a video recommendation device, electronic equipment and a storage medium, comprising the following steps: dividing behavior data acquired by the mobile equipment in a set time period when the mobile equipment is operated by a user with one hand to obtain at least one data set, wherein different data sets belong to different time slices; determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set; when video recommendation is determined to be carried out on the user, determining target behavior characteristics matched with the current time from the determined behavior characteristics corresponding to the user in each time segment; and sequencing the recommended videos according to the target behavior characteristics, and recommending the videos to the user according to the sequencing result. Therefore, the transverse arrangement sequence of the recommended videos on the video playing interface can be dynamically determined according to the single-hand operation behaviors of the user in different time slices, the operation experience of the user is improved, and the click playing quantity of the recommended videos is further improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a video recommendation method, a video recommendation device, electronic equipment and a storage medium.
Background
Currently, when a user operates a mobile phone, the user usually uses the lower half of the mobile phone to hold the mobile phone with a single hand, and uses the thumb to perform touch control on a touch display screen of the mobile phone, for example, clicking a video icon on a video application interface to trigger video playing.
However, in the above-mentioned use scenario, because the length of the thumb of the user is limited, the touchable area of the touch display screen is limited, which results in that the user uses the thumb to click on the video application interface, and the user is relatively hard to hold the video icon far from the user's hand, thereby affecting the operation experience of the user, and further affecting the click play amount of the video.
Disclosure of Invention
In view of this, in order to solve the technical problem that in the prior art, when a user operates a mobile device with one hand in a video recommendation scene, the embodiment of the invention provides a video recommendation method, a device, an electronic device and a storage medium.
In a first aspect, an embodiment of the present invention provides a video recommendation method, including:
dividing behavior data acquired by mobile equipment in a set time period when a user operates the mobile equipment by one hand to obtain at least one data set, wherein different data sets belong to different time slices;
Determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set, wherein the behavior characteristics are used for representing a human body part used by the user when the user operates the mobile device in a single hand in the corresponding time segment, and the human body part comprises a left hand or a right hand of the human body;
when video recommendation is determined to be carried out on the user, determining target behavior characteristics matched with the current time from the determined behavior characteristics corresponding to the user in each time segment;
and sequencing the recommended videos according to the target behavior characteristics, and recommending the videos to the user according to the sequencing result.
In one possible implementation manner, the dividing the behavior data acquired by the mobile device in a set period of time when the user operates the mobile device with one hand to obtain at least one data set includes:
dividing the set time period according to the set time interval to obtain a plurality of time slices;
determining corresponding time slices according to the acquisition time of each behavior data, dividing the behavior data corresponding to the same time slice into the same data set, and dividing the behavior data corresponding to different time slices into different data sets.
In one possible implementation manner, the determining the behavior characteristics corresponding to the user in each time segment according to the behavior data in each data set includes:
for each behavior data in each data set, determining a human body part used when the user operates the mobile device by one hand according to the behavior data;
determining a first time for the user to operate the mobile device by using the left hand of the human body and a second time for the user to operate the mobile device by using the right hand of the human body in a time slice to which the data set belongs;
and determining corresponding behavior characteristics of the user in the time segment to which the data set belongs based on the first times and the second times.
In one possible embodiment, the behavior data includes: starting position coordinates and ending position coordinates of the mobile device on a display screen of the mobile device in a one-hand sliding mode;
the determining, according to the behavior data, a human body part used when the user operates the mobile device with one hand, including:
calculating the initial position coordinates and the final position coordinates according to a set angle calculation mode to obtain a first angle;
if the first angle meets a first setting condition, determining that a human body part used when the user operates the mobile equipment by one hand is a left hand of a human body;
And if the first angle meets a second setting condition, determining that the human body part used when the user operates the mobile equipment by one hand is the right hand of the human body.
In one possible implementation manner, the determining, from the determined behavior characteristics corresponding to the user in each time slot, a target behavior characteristic matching with the current time includes:
determining a target time segment to which the current time belongs according to the starting time and the ending time of each time segment;
and determining the determined behavior characteristics of the user corresponding to the target time segment as the target behavior characteristics.
In one possible implementation manner, the ranking the video to be recommended according to the target behavior feature includes:
if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment by one hand in the target time segment is the left hand of the human body, sequencing the recommended videos according to the sequence from high to low of video playing heat;
and if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment by one hand in the target time segment is the right hand of the human body, sequencing the recommended videos according to the sequence of low video playing heat.
In a second aspect, an embodiment of the present invention provides a video recommendation apparatus, including:
the data dividing module is used for dividing behavior data acquired by the mobile equipment in a set time period when the user operates the mobile equipment by one hand to obtain at least one data set, wherein different data sets belong to different time slices;
the first determining module is used for determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set, wherein the behavior characteristics are used for representing a human body part used by the user when the mobile device is operated by one hand in the corresponding time segment, and the human body part comprises a left hand or a right hand of a human body;
the second determining module is used for determining target behavior characteristics matched with the current time from the determined behavior characteristics of the user corresponding to the time slices when video recommendation is determined to the user;
and the video recommendation module is used for sequencing the recommended videos according to the target behavior characteristics and recommending the videos to the user according to the sequencing result.
In one possible implementation manner, the data dividing module divides behavior data acquired by the mobile device in a set period of time when a user operates the mobile device with one hand, so as to obtain at least one data set, including:
Dividing the set time period according to the set time interval to obtain a plurality of time slices;
determining corresponding time slices according to the acquisition time of each behavior data, dividing the behavior data corresponding to the same time slice into the same data set, and dividing the behavior data corresponding to different time slices into different data sets.
In a possible implementation manner, the first determining module determines, according to the row data in each data set, a corresponding behavior characteristic of the user in each time segment, including:
for each behavior data in each data set, determining a human body part used when the user operates the mobile device by one hand according to the behavior data;
determining a first time for the user to operate the mobile device by using the left hand of the human body and a second time for the user to operate the mobile device by using the right hand of the human body in a time slice to which the data set belongs;
and determining corresponding behavior characteristics of the user in the time segment to which the data set belongs based on the first times and the second times.
In a possible implementation manner, the behavior data includes: starting position coordinates and ending position coordinates of the mobile device on a display screen of the mobile device in a one-hand sliding mode;
The first determining module determines a human body part used when the user operates the mobile device by one hand according to the behavior data, and the first determining module comprises:
calculating the initial position coordinates and the final position coordinates according to a set angle calculation mode to obtain a first angle;
if the first angle meets a first setting condition, determining that a human body part used when the user operates the mobile equipment by one hand is a left hand of a human body;
and if the first angle meets a second setting condition, determining that the human body part used when the user operates the mobile equipment by one hand is the right hand of the human body.
In a possible implementation manner, the second determining module determines, from the determined behavior characteristics corresponding to the user in each time segment, a target behavior characteristic matched with the current time, including:
determining a target time segment to which the current time belongs according to the starting time and the ending time of each time segment;
and determining the determined behavior characteristics of the user corresponding to the target time segment as the target behavior characteristics.
In a possible implementation manner, the video recommendation module ranks the videos to be recommended according to the target behavior feature, including:
If the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment in the target time segment by one hand is the left hand of the human body, sequencing the recommended videos according to the sequence of the recommendation priority from high to low;
and if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment by one hand in the target time segment is the right hand of the human body, sequencing the recommended videos according to the sequence of the recommended priority from low to high.
In a third aspect, an embodiment of the present invention provides an electronic device, including: the video recommendation method according to any one of the first aspect, comprising a processor and a memory, the processor being configured to execute a video recommendation program stored in the memory.
In a fourth aspect, an embodiment of the present invention provides a storage medium storing one or more programs executable by one or more processors to implement the video recommendation method of any one of the first aspects.
According to the technical scheme provided by the embodiment of the invention, the behavior data of the mobile equipment, which is acquired in a set time period and is used for operating the mobile equipment by a user in one hand, is divided to obtain at least one data set, different data sets belong to different time slices, corresponding behavior characteristics of the user in each time slice are determined according to the behavior data in each data set, the behavior characteristics are used for representing the human body part used by the user when operating the mobile equipment by the user in one hand in the corresponding time slice, and the human body part comprises the left hand or the right hand of the human body, so that the single-hand operation behavior habit of the user in different time periods is obtained.
Further, when video recommendation is determined to be performed on the user, from the determined behavior characteristics of the user corresponding to each time slice, determining target behavior characteristics matched with the current time, sorting the recommended videos according to the target behavior characteristics, and performing video recommendation to the user according to the sorting result, so that the transverse arrangement sequence of the recommended videos on the video playing interface is dynamically determined according to the single-hand operation behavior habit of the user in the time slice to which the current time belongs, the user can easily click on the recommended videos of interest, the operation experience of the user is improved, and the click playing quantity of the recommended videos is further improved.
Drawings
Fig. 1 is a schematic view of an application scenario of a video recommendation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of an embodiment of a video recommendation method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a sliding track of a one-handed sliding operation when a user slides on a touch display screen of a mobile device using a thumb of the left hand;
FIG. 4 is a schematic diagram of a sliding track of a one-handed sliding operation when a user slides on a touch display screen of a mobile device using a right-handed thumb;
Fig. 5 is a block diagram of an embodiment of a video recommendation device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to facilitate understanding of the embodiments of the present invention, an application scenario of the video recommendation method provided by the embodiments of the present invention is first explained with reference to the accompanying drawings.
Referring to fig. 1, an application scenario schematic diagram of a video recommendation method according to an embodiment of the present invention is provided.
As shown in fig. 1, a user 101 operates a mobile device 102 using a single hand, such as the left hand, to control the mobile device 102 to provide network services. Further, in the video recommendation scenario, a video application (not shown in fig. 1) installed on the mobile device 102 may also recommend other videos (hereinafter referred to as recommended videos) to the user during the process of playing the video. Optionally, the recommended video is determined by a server to which the video application corresponds, and there is some association with the video that the video application is currently playing. For example, the recommended video and the video currently being played belong to the same episode, and for example, the recommended video and the video currently being played belong to the same type, or have the same video tag, and the embodiment of the present invention is not limited thereto. As to how the server determines recommended videos, the present invention is not concerned.
In one embodiment, the video application may enable video recommendation to the user by presenting a video icon of the recommended video on a video playback interface.
In one example, the video application may present video icons of multiple recommended videos laterally on the video playback interface. For example, as shown in fig. 1, video icons of recommended video 1 to recommended video 4 are presented laterally on the video playback interface.
Further, in the conventional method, when a plurality of recommended videos are displayed laterally on a video playing interface, the arrangement rule of the recommended videos is fixed. Such as ordering the recommended videos in order of high video play heat or in order of small episodes at any time in any scene, which results in different operation experiences when clicking on the same recommended video in two different scenes, i.e., a user operating the mobile device 102 with the left hand and a user operating the mobile device 102 with the right hand. For example, the user tends to watch the recommended video with highest video playing heat, so in a scenario where the user uses the left hand to operate the mobile device 102, the user can easily click on a video icon that wants to watch the video, while in a scenario where the user uses the right hand to operate the mobile device 102, the user is more laborious to click on the video icon that wants to watch the video, thus causing inconvenience in operation, affecting the operation experience of the user, and further, the user may change the viewing intention due to inconvenience in operation, thereby affecting the click play amount of the video.
Based on the above, the embodiment of the invention provides a video recommendation method, by which the transverse arrangement sequence of recommended videos on a video playing interface can be dynamically determined according to different single-hand operation behaviors of a user in different time periods, so that the operation experience of the user is improved, and the click playing amount of the recommended videos is further improved.
The following description will proceed with reference being made to the drawings, which are not intended to limit the scope of embodiments of the invention.
Referring to fig. 2, a flowchart of an embodiment of a video recommendation method is provided in an embodiment of the present invention. In one example, the method is applied to an electronic device, where the electronic device may be a hardware device that supports network connection to provide various network services, including but not limited to a smart phone, a tablet computer, a laptop computer, a desktop computer, a server, etc., where when the electronic device is a server, the electronic device may be a server corresponding to a video application in the application scenario shown in fig. 1. As shown in fig. 2, the method comprises the steps of:
First, some conceptual terms involved in this step 201 will be explained:
(1) Setting a time period:
typically, it is likely that the same user will have the same behavior for the same time period of each day. For example, users share lunch at 12 to 12 points 30 per day, and during this period, typically use the right hand for eating and the left hand for operating the mobile device (e.g., a smart phone); as another example, the user takes a bus at 8 to 9 points per day, and during this time, typically uses the right hand to operate the mobile device. It follows that the body parts used when operating the mobile device with one hand during the same time period of the day are likely to be the same for the same user, where the body parts include a left hand of the human body (hereinafter referred to as left hand) and a right hand of the human body (hereinafter referred to as right hand). Based on this, the above-mentioned set period of time may be set to 0 point to 24 points of a certain day, or a period of time within a certain day, such as 6 points to 24 points.
(2) Time segment:
the above-described time slice refers to one of the set time periods.
In the embodiment of the invention, the set time period can be divided according to the set time interval to obtain a plurality of time slices. For example, assuming that the set time period is 6 to 24 points and assuming that the set time interval is 1 hour, the set time period is divided according to the set time interval to obtain 18 time slices, and the 18 time slices respectively correspond to 6 to 7 points, 7 to 8 points, 8 to 9 points, and so on.
(2) Single hand operation:
in the embodiment of the present invention, the one-handed operation refers to a sliding operation, such as a sliding operation performed on a touch display screen of a mobile device using a thumb.
(3) Behavior data:
corresponding to the concept of one-handed operation described above, the behavior data may include start position coordinates and end position coordinates of a one-handed sliding operation on the touch display screen of the mobile device.
For example, as shown in fig. 3, a schematic diagram of a sliding track of a one-hand sliding operation is shown when a user slides on a touch display screen of a mobile device using a thumb of the left hand. In fig. 3, a coordinate system is established with the top left corner vertex of the touch display screen of the mobile device as the origin of coordinates, the horizontal right direction as the positive X-axis direction, and the vertical downward direction as the positive Y-axis direction, in which the initial position coordinates of the sliding track are P1 (X1, Y1) and the final position coordinates are P2 (X2, Y2).
For another example, as shown in fig. 4, a schematic diagram of a sliding track of a one-hand sliding operation is shown when a user slides on a touch display screen of a mobile device using a thumb of the right hand. In fig. 4, a coordinate system is still established with the top left corner vertex of the touch display screen of the mobile device as the origin of coordinates, with the horizontal right direction as the positive X-axis direction, and the vertical downward direction as the positive Y-axis direction, in which the coordinates of the start position of the sliding track are P1 (X1, Y1) and the coordinates of the end position are P2 (X2, Y2).
In an embodiment, the behavioral data may also include user identification, acquisition time, and the like.
The specific implementation of this step 201 is explained below:
(1) The mobile device collects an explanation of data of a user when the mobile device is operated by one hand in a set time period:
in an embodiment, the mobile device may collect behavior data of a user operating the mobile device with one hand in a set period of time based on an externally input collection instruction. It will be appreciated that in this embodiment, the acquisition instructions described above may be carried for a set period of time.
In an exemplary scenario, after the video application is downloaded for the first time, the user may input the above-mentioned acquisition instruction to the mobile device through the functional interface provided by the video application, so as to instruct the mobile device to acquire behavior data when the user operates the mobile device with one hand in a set period of time.
In another embodiment, the mobile device may periodically collect behavior data when the user operates the mobile device with one hand for a set period of time. For example, the mobile device may collect behavior data during the first day of each month when the user operates the mobile device with one hand during that day, i.e., the first day of each month.
Further, in an embodiment, the mobile device may save behavior data collected during a set period of time when the user operates the mobile device with one hand, so that the mobile device may execute the video recommendation method provided by the present invention.
Under the condition that the mobile device periodically collects behavior data when a user operates the mobile device by one hand within a set period of time, the mobile device updates the behavior data stored by the local terminal every time a collection period is passed, namely, the mobile device only stores the latest collected behavior data. The video recommendation method and the video recommendation system can be used for recommending videos to users based on the latest behavior habits of the users, and better meets user operation experience.
In another embodiment, the mobile device may send the behavior data collected during the set period of time when the user operates the mobile device with one hand to the corresponding server, so that the server may execute the video recommendation method provided by the present invention.
Under the condition that the mobile device periodically collects behavior data when a user operates the mobile device by one hand in a set period of time, the server deletes the behavior data of the user received last time when the server receives the behavior data of the same user, namely, the server only stores the latest collected behavior data of the mobile device. The video recommendation method and the video recommendation system can achieve video recommendation to the user based on the latest behavior habit of the user, and better meet user operation experience.
(2) Dividing behavior data acquired by the mobile device in a set time period when the mobile device is operated by a user with one hand, and obtaining an explanation of at least one data set:
first, in the case where the video recommendation method provided by the present invention is executed by the server, the server side may store behavior data of a plurality of users transmitted by a plurality of mobile devices, and based on this, in step 201, the server may determine behavior data of the same user based on the user identifier, and then divide the behavior data of the same user.
In an embodiment, for each pedestrian data, a corresponding time slice is determined according to the acquisition time of the behavior data, the behavior data corresponding to the same time slice is divided into the same data set, and the behavior data corresponding to different time slices is divided into different data sets, so that different data sets belong to different time slices.
The above-mentioned behavioral characteristics are used to represent the human body parts used by the user when operating the mobile device with one hand in the corresponding time segment.
In one embodiment, taking one of the data sets as an example, determining, according to behavior data in the data set, a behavior feature corresponding to a user in a time segment to which the data set belongs includes the following steps:
a1, aiming at each behavior data in the data set, determining a human body part used when a user operates the mobile device by one hand according to the behavior data.
In the step a1, the initial position coordinate and the final position coordinate may be calculated according to a set angle calculation mode to obtain a first angle, and then the human body part used when the user operates the mobile device with one hand may be determined according to the empirical value and the magnitude of the first angle.
Wherein, the set angle calculation formula is shown in the following formula (one):
as shown in fig. 3 and 4, the first angle calculated according to the above formula (one) is +.a in fig. 3 and 4.
In an embodiment, if the first angle satisfies a first setting condition of the following example, determining a human body part used when the user operates the mobile device with one hand as a left hand of the human body; if the first angle satisfies a second setting condition of the following example, it is determined that a human body part used when the user operates the mobile device with one hand is a human right hand.
First setting conditions: beta-alpha less than or equal to beta a is less than or equal to less than beta + < alpha;
second setting conditions: the angle beta-angle alpha is less than or equal to 180 DEG-angle a is less than or equal to beta plus-angle alpha;
the +.beta.and +.alpha.are preset empirical values.
a2, determining a first time for operating the mobile device by using the left hand of the human body and a second time for operating the mobile device by using the right hand of the human body in a time segment to which the data set belongs.
a3, determining the behavior characteristics of the user in the time segment to which the data set belongs based on the first times and the second times.
In an embodiment, the first number of times and the second number of times are compared, if the first number of times is greater than the second number of times, this means that the user tends to operate the mobile device with the left hand during the time segment to which the data set belongs, and thus the behavior characteristics of the user during the time segment to which the data set belongs indicate that the human body part used by the user when operating the mobile device with one hand during the time segment to which the data set belongs is the left hand. Conversely, if the second number is greater than the first number, it means that the user tends to operate the mobile device with the right hand during the time segment to which the data set belongs, and thus, the behavior characteristics of the user during the time segment to which the data set belongs indicate that the human body part used by the user when operating the mobile device with the single hand during the time segment to which the data set belongs is the right hand.
In another embodiment, the weight ratio P1 of the first number is calculated by the following formula (two), if the calculated weight ratio P1 is greater than or equal to the preset weight ratio threshold, it means that the user tends to use the left hand to operate the mobile device in the time segment to which the data set belongs, and therefore, the behavior feature of the user in the time segment to which the data set belongs indicates that the human body part used by the user when the user operates the mobile device in one hand in the time segment to which the data set belongs is left hand; conversely, if the calculated weight ratio P1 is smaller than the preset weight ratio threshold, it means that the user tends to operate the mobile device with the right hand in the time segment to which the data set belongs, and therefore, the behavior feature of the user in the time segment to which the data set belongs indicates that the human body part used by the user when operating the mobile device with the single hand in the time segment to which the data set belongs is the right hand.
In the above formula (two), S1 represents a first number of times and S2 represents a second number of times.
In yet another embodiment, the weight ratio P2 of the second number is calculated by the following formula (iii), if the calculated weight ratio P2 is equal to or greater than the preset weight ratio threshold, it means that the user tends to use the right hand to operate the mobile device in the time segment to which the data set belongs, and thus, the behavior characteristics of the user in the time segment to which the data set belongs indicate that the human body part used by the user when operating the mobile device in one hand in the time segment to which the data set belongs is the right hand; conversely, if the calculated weight ratio P1 is smaller than the preset weight ratio threshold, it means that the user tends to operate the mobile device with the left hand in the time segment to which the data set belongs, and therefore, the behavior feature of the user in the time segment to which the data set belongs indicates that the human body part used by the user when operating the mobile device with the single hand in the time segment to which the data set belongs is the left hand.
Furthermore, in an embodiment, after step 202 is performed, the behavior characteristics corresponding to the user in each time slot may be stored in a key-value storage format. For example, the corresponding behavior characteristics of the user in each time segment are stored as (T1, 1/0), (T2, 1/0), (T3, 1/0), … … and (Tn, 1/0), wherein T1 to Tn represent n time segments, 1 represents a human body part used by the user when operating the mobile device with one hand in the corresponding time segment is a left hand, and 0 represents a human body part used by the user when operating the mobile device with one hand in the corresponding time segment is a right hand. Through the storage mode, the corresponding behavior characteristics of the user in a certain time segment can be conveniently and subsequently determined.
In step 203, a time slice (hereinafter referred to as a target time slice) to which the current time belongs is first determined according to the start time and the end time of each time slice, and then the determined behavior feature of the user corresponding to the target time slice is determined as the target behavior feature.
And 204, sorting the recommended videos according to the target behavior characteristics, and recommending the videos to the user according to the sorting result.
It can be understood that if the target behavior feature indicates that the human body part used by the user when operating the mobile device with one hand in the target time segment is the left hand of the human body, this means that the more the display position of the video icon of the recommended video on the interface is biased to the left side, the more convenient the user clicks the video icon; otherwise, if the target behavior feature indicates that the human body part used when the user operates the mobile device with one hand in the target time segment is the right hand of the human body, this means that the more the display position of the video icon of the recommended video on the interface is deviated to the right side, the more convenient the user clicks the video icon. Therefore, the ranking rules of the recommended videos are different under different target behavior characteristics.
In an embodiment, if the target behavior feature indicates that the human body part used by the user when operating the mobile device with one hand in the target time segment is the left hand of the human body, the recommended videos are ranked according to the order of the recommendation priority from high to low. The method and the device can realize that the recommendation priorities of the displayed recommended videos are arranged from low to high on the interface according to the order from right to left, namely, the recommendation priority is higher when the recommended videos are closer to the left, and the higher the recommendation priority is, the higher the watching interest of a user in the recommended videos is indicated, so that the user can easily click on interesting recommended videos when using the left-handed operation mobile device, and the user operation experience is improved.
And if the target behavior characteristic indicates that the human body part used by the user when operating the mobile device by one hand in the target time segment is the right hand of the human body, sequencing the recommended videos according to the sequence of the recommendation priority from low to high. The method can realize that the recommendation priorities of the displayed recommended videos are arranged from low to high on the interface according to the left-to-right sequence, namely, the recommendation priorities are higher when the recommended videos are closer to the right side, so that a user can easily click on interesting recommended videos when using the right hand to operate the mobile device, and the user operation experience is improved.
In one example, the recommendation priority may be represented by a video playing heat, and in particular, the higher the video playing heat, the higher the recommendation priority.
In another example, the recommendation priority may be represented by a video relevance, where the video relevance refers to a relevance between a recommended video and a video currently being played, and in particular, the higher the video relevance, the higher the recommendation priority.
It should be noted that the above two examples are merely illustrative examples of the recommendation priority, and in practical applications, the recommendation priority may also be related to other parameters, which is not limited by the present invention.
Thus, the description of the flow shown in fig. 2 is completed.
According to the technical scheme provided by the embodiment of the invention, the behavior data of the mobile equipment, which is acquired in a set time period and is used for operating the mobile equipment by a user in one hand, is divided to obtain at least one data set, different data sets belong to different time slices, corresponding behavior characteristics of the user in each time slice are determined according to the behavior data in each data set, the behavior characteristics are used for representing the human body part used by the user when operating the mobile equipment by the user in one hand in the corresponding time slice, and the human body part comprises the left hand or the right hand of the human body, so that the single-hand operation behavior habit of the user in different time periods is obtained.
Further, when video recommendation is determined to be performed on the user, from the determined behavior characteristics of the user corresponding to each time slice, determining target behavior characteristics matched with the current time, sorting the recommended videos according to the target behavior characteristics, and performing video recommendation to the user according to the sorting result, so that the transverse arrangement sequence of the recommended videos on the video playing interface is dynamically determined according to the single-hand operation behavior habit of the user in the time slice to which the current time belongs, the user can easily click on the recommended videos of interest, the operation experience of the user is improved, and the click playing quantity of the recommended videos is further improved.
The invention also provides an embodiment of the video recommending device corresponding to the embodiment of the video recommending method.
As shown in fig. 5, a block diagram of an embodiment of a video recommendation apparatus according to an embodiment of the present invention includes: a data dividing module 51, a first determining module 52, a second determining module 53, and a video recommending module 54.
The data dividing module 51 is configured to divide behavior data acquired by the mobile device in a set time period when the user operates the mobile device with one hand, so as to obtain at least one data set, where different data sets belong to different time slices;
a first determining module 52, configured to determine, according to the behavior data in each of the data sets, a behavior characteristic corresponding to each of the time slices, where the behavior characteristic is used to represent a human body part used by the user when operating the mobile device with one hand in the corresponding time slice, and the human body part includes a left hand or a right hand of a human body;
a second determining module 53, configured to determine, when determining that video recommendation is performed on the user, a target behavior feature matched with the current time from the determined behavior features of the user corresponding to the time segments;
The video recommendation module 54 is configured to rank recommended videos according to the target behavior feature, and to recommend videos to the user according to the ranking result.
In one possible implementation manner, the data dividing module 51 divides behavior data acquired by the mobile device during a set period of time when the user operates the mobile device with one hand, so as to obtain at least one data set, including:
dividing the set time period according to the set time interval to obtain a plurality of time slices;
determining corresponding time slices according to the acquisition time of each behavior data, dividing the behavior data corresponding to the same time slice into the same data set, and dividing the behavior data corresponding to different time slices into different data sets.
In a possible implementation manner, the first determining module 52 determines, according to the behavior data in each data set, a behavior characteristic corresponding to the user in each time segment, including:
for each behavior data in each data set, determining a human body part used when the user operates the mobile device by one hand according to the behavior data;
determining a first time for the user to operate the mobile device by using the left hand of the human body and a second time for the user to operate the mobile device by using the right hand of the human body in a time slice to which the data set belongs;
And determining corresponding behavior characteristics of the user in the time segment to which the data set belongs based on the first times and the second times.
In a possible implementation manner, the behavior data includes: starting position coordinates and ending position coordinates of the mobile device on a display screen of the mobile device in a one-hand sliding mode;
the first determining module 52 determines a human body part used when the user operates the mobile device with one hand according to the behavior data, including:
calculating the initial position coordinates and the final position coordinates according to a set angle calculation mode to obtain a first angle;
if the first angle meets a first setting condition, determining that a human body part used when the user operates the mobile equipment by one hand is a left hand of a human body;
and if the first angle meets a second setting condition, determining that the human body part used when the user operates the mobile equipment by one hand is the right hand of the human body.
In a possible implementation manner, the second determining module 53 determines, from the determined behavior characteristics of the user corresponding to the time segments, a target behavior characteristic matching the current time, including:
determining a target time segment to which the current time belongs according to the starting time and the ending time of each time segment;
And determining the determined behavior characteristics of the user corresponding to the target time segment as the target behavior characteristics.
In one possible implementation, the video recommendation module 54 ranks the videos to be recommended according to the target behavior feature, including:
if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment in the target time segment by one hand is the left hand of the human body, sequencing the recommended videos according to the sequence of the recommendation priority from high to low;
and if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment by one hand in the target time segment is the right hand of the human body, sequencing the recommended videos according to the sequence of the recommended priority from low to high.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and an electronic device 600 shown in fig. 6 includes: at least one processor 601, memory 602, at least one network interface 604, and other user interfaces 603. The various components in the electronic device 600 are coupled together by a bus system 605. It is understood that the bus system 605 is used to enable connected communications between these components. The bus system 605 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration the various buses are labeled as bus system 605 in fig. 6.
The user interface 603 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, etc.).
It is to be appreciated that the memory 602 in embodiments of the invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be a Read-only memory (ROM), a programmable Read-only memory (ProgrammableROM, PROM), an erasable programmable Read-only memory (ErasablePROM, EPROM), an electrically erasable programmable Read-only memory (ElectricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be a random access memory (RandomAccessMemory, RAM) that acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic random access memory (DynamicRAM, DRAM), synchronous dynamic random access memory (SynchronousDRAM, SDRAM), double data rate synchronous dynamic random access memory (ddr SDRAM), enhanced Synchronous Dynamic Random Access Memory (ESDRAM), synchronous link dynamic random access memory (SynchlinkDRAM, SLDRAM), and direct memory bus random access memory (DirectRambusRAM, DRRAM). The memory 602 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some implementations, the memory 602 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 6021 and application programs 6022.
The operating system 6021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 6022 includes various application programs such as a media player (MediaPlayer), a Browser (Browser), and the like for realizing various application services. The program for implementing the method of the embodiment of the present invention may be contained in the application program 6022.
In the embodiment of the present invention, the processor 601 is configured to execute the method steps provided by the method embodiments by calling a program or an instruction stored in the memory 602, specifically, a program or an instruction stored in the application 6022, for example, including:
dividing behavior data acquired by mobile equipment in a set time period when a user operates the mobile equipment by one hand to obtain at least one data set, wherein different data sets belong to different time slices;
determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set, wherein the behavior characteristics are used for representing a human body part used by the user when the user operates the mobile device in a single hand in the corresponding time segment, and the human body part comprises a left hand or a right hand of the human body;
When video recommendation is determined to be carried out on the user, determining target behavior characteristics matched with the current time from the determined behavior characteristics corresponding to the user in each time segment;
and sequencing the recommended videos according to the target behavior characteristics, and recommending the videos to the user according to the sequencing result.
The method disclosed in the above embodiment of the present invention may be applied to the processor 601 or implemented by the processor 601. The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be accomplished by integrated logic circuitry of hardware in the processor 601 or instructions in the form of software. The processor 601 may be a general purpose processor, a digital signal processor (DigitalSignalProcessor, DSP), an application specific integrated circuit (application specific IntegratedCircuit, ASIC), an off-the-shelf programmable gate array (FieldProgrammableGateArray, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software elements in a decoding processor. The software elements may be located in a random access memory, flash memory, read only memory, programmable read only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 602, and the processor 601 reads information in the memory 602 and performs the steps of the above method in combination with its hardware.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ApplicationSpecificIntegratedCircuits, ASIC), digital signal processors (DigitalSignalProcessing, DSP), digital signal processing devices (dspev), programmable logic devices (ProgrammableLogicDevice, PLD), field programmable gate arrays (Field-ProgrammableGateArray, FPGA), general purpose processors, controllers, micro controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
The electronic device provided in this embodiment may be an electronic device as shown in fig. 6, and may perform all steps of the video recommendation method as shown in fig. 2, so as to achieve the technical effects of the video recommendation method as shown in fig. 2, and the detailed description with reference to fig. 2 is omitted herein for brevity.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium here stores one or more programs. Wherein the storage medium may comprise volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid state disk; the memory may also comprise a combination of the above types of memories.
When the one or more programs are executed by the one or more processors, the automatic printing method executed on the electronic device side is realized.
The processor is used for executing the video recommendation program stored in the memory to realize the following steps of the video recommendation method executed on the electronic equipment side:
dividing behavior data acquired by mobile equipment in a set time period when a user operates the mobile equipment by one hand to obtain at least one data set, wherein different data sets belong to different time slices;
determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set, wherein the behavior characteristics are used for representing a human body part used by the user when the user operates the mobile device in a single hand in the corresponding time segment, and the human body part comprises a left hand or a right hand of the human body;
When video recommendation is determined to be carried out on the user, determining target behavior characteristics matched with the current time from the determined behavior characteristics corresponding to the user in each time segment;
and sequencing the recommended videos according to the target behavior characteristics, and recommending the videos to the user according to the sequencing result.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A video recommendation method, comprising:
dividing behavior data acquired by mobile equipment in a set time period when a user operates the mobile equipment by one hand to obtain at least one data set, wherein different data sets belong to different time slices;
determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set, wherein the behavior characteristics are used for representing a human body part used by the user when the user operates the mobile device in a single hand in the corresponding time segment, and the human body part comprises a left hand or a right hand of the human body;
when video recommendation is determined to be carried out on the user, determining target behavior characteristics matched with the current time from the determined behavior characteristics corresponding to the user in each time segment;
And sequencing the recommended videos according to the target behavior characteristics, and recommending the videos to the user according to the sequencing result.
2. The method of claim 1, wherein the dividing behavior data collected by the mobile device during a set period of time when the mobile device is operated by a user with one hand to obtain at least one data set includes:
dividing the set time period according to the set time interval to obtain a plurality of time slices;
determining corresponding time slices according to the acquisition time of each behavior data, dividing the behavior data corresponding to the same time slice into the same data set, and dividing the behavior data corresponding to different time slices into different data sets.
3. The method of claim 1, wherein said determining the corresponding behavioral characteristics of the user within each of the time segments from the behavioral data in each of the data sets comprises:
for each behavior data in each data set, determining a human body part used when the user operates the mobile device by one hand according to the behavior data;
determining a first time for the user to operate the mobile device by using the left hand of the human body and a second time for the user to operate the mobile device by using the right hand of the human body in a time slice to which the data set belongs;
And determining corresponding behavior characteristics of the user in the time segment to which the data set belongs based on the first times and the second times.
4. A method according to claim 3, wherein the behavioral data comprises: starting position coordinates and ending position coordinates of the mobile device on a display screen of the mobile device in a one-hand sliding mode;
the determining, according to the behavior data, a human body part used when the user operates the mobile device with one hand, including:
calculating the initial position coordinate and the final position coordinate according to a set angle calculation mode to obtain a first angle;
if the first angle meets a first setting condition, determining that a human body part used when the user operates the mobile equipment by one hand is a left hand of a human body;
and if the first angle meets a second setting condition, determining that the human body part used when the user operates the mobile equipment by one hand is the right hand of the human body.
5. The method of claim 1, wherein said determining a target behavioral characteristic that matches the current time from the determined behavioral characteristics of the user corresponding within each of the time segments comprises:
determining a target time segment to which the current time belongs according to the starting time and the ending time of each time segment;
And determining the determined behavior characteristics of the user corresponding to the target time segment as the target behavior characteristics.
6. The method of claim 5, wherein the ranking the videos to be recommended according to the target behavioral characteristics comprises:
if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment by one hand in the target time segment is the left hand of the human body, sequencing the recommended videos according to the sequence of the recommendation priority from high to low;
and if the target behavior characteristic indicates that the human body part used by the user when operating the mobile equipment by one hand in the target time segment is the right hand of the human body, sequencing the recommended videos according to the sequence of the recommendation priority from low to high.
7. A video recommendation device, comprising:
the data dividing module is used for dividing behavior data acquired by the mobile equipment in a set time period when the mobile equipment is operated by a user with one hand to obtain at least one data set, wherein different data sets belong to different time slices;
the first determining module is used for determining corresponding behavior characteristics of the user in each time segment according to the behavior data in each data set, wherein the behavior characteristics are used for representing a human body part used by the user when the user operates the mobile device in a single hand in the corresponding time segment, and the human body part comprises a left hand or a right hand of a human body;
The second determining module is used for determining target behavior characteristics matched with the current time from the determined behavior characteristics of the user corresponding to the time slices when video recommendation is determined to the user;
and the video recommendation module is used for sequencing the recommended videos according to the target behavior characteristics and recommending the videos to the user according to the sequencing result.
8. The apparatus of claim 7, wherein the first determining module determines, from the behavioral data in each of the data sets, a corresponding behavioral characteristic of the user within each of the time segments, comprising:
for each behavior data in each data set, determining a human body part used when the user operates the mobile device by one hand according to the behavior data;
determining a first time for the user to operate the mobile device by using the left hand of the human body and a second time for the user to operate the mobile device by using the right hand of the human body in a time slice to which the data set belongs;
and determining corresponding behavior characteristics of the user in the time segment to which the data set belongs based on the first times and the second times.
9. An electronic device, comprising: a processor and a memory, the processor being configured to execute a video recommendation program stored in the memory to implement the video recommendation method of any one of claims 1 to 6.
10. A storage medium storing one or more programs executable by one or more processors to implement the video recommendation method of any one of claims 1-6.
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