CN106649848B - Video recommendation method and device - Google Patents
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Abstract
The disclosure relates to a video recommendation method and device. The method comprises the following steps: determining a label corresponding to a video currently watched by a user; determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos; sequencing the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sequencing result; and recommending the videos to be recommended according to the sequencing result. According to the method and the device, when the video related to the video watched by the user at present is recommended, the label corresponding to the user is considered, and therefore the video recommendation effect can be improved.
Description
Technical Field
The present disclosure relates to the field of video technologies, and in particular, to a video recommendation method and apparatus.
Background
When a user watches a video through a video website, the video website will generally recommend to the user a video that is related to the video currently watched by the user. For example, when the user views a video in a non-full screen mode, information of the video related to the video currently viewed by the user may be displayed on the right and/or lower side of the video play window. The quality of the video recommendation effect is mainly judged according to the click rate, and generally, the higher the click rate is, the better the video recommendation effect is. In the related art, video recommendation is usually performed according to the title and/or the label of the video currently watched by the user, and the related information is considered less, so that the video recommendation effect is poor.
Disclosure of Invention
Technical problem
In view of the above, the technical problem to be solved by the present disclosure is to provide a video recommendation method and system.
Solution scheme
In order to solve the above technical problem, according to an embodiment of the present disclosure, there is provided a video recommendation method including:
determining a label corresponding to a video currently watched by a user;
determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos;
sequencing the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sequencing result;
and recommending the videos to be recommended according to the sequencing result.
For the above method, in a possible implementation manner, sorting the videos to be recommended according to the tag corresponding to the currently viewed video, the tags corresponding to the videos to be recommended, and the tag corresponding to the user to obtain a sorting result includes:
for each video to be recommended, determining a ranking value of the video to be recommended according to the label corresponding to the currently watched video, the label corresponding to the video to be recommended and the label corresponding to the user;
and sequencing the videos to be recommended according to the sequencing value to obtain a sequencing result.
For the above method, in a possible implementation manner, for each video to be recommended, determining a ranking value of the video to be recommended according to the tag corresponding to the currently viewed video, the tag corresponding to the video to be recommended, and the tag corresponding to the user respectively includes:
for each video to be recommended, determining a predicted value of the video to be recommended according to the label corresponding to the currently watched video and the label corresponding to the video to be recommended respectively;
and adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ranking value of the video to be recommended.
For the above method, in a possible implementation manner, adjusting the predicted value of the video to be recommended according to the tag corresponding to the user includes:
calculating the similarity between the label corresponding to the user and the label corresponding to the video to be recommended;
and adjusting the predicted value of the video to be recommended according to the similarity to obtain the ranking value of the video to be recommended.
For the above method, in a possible implementation manner, adjusting the predicted value of the video to be recommended according to the similarity to obtain the ranking value of the video to be recommended includes:
determining the weight corresponding to the predicted value and the weight corresponding to the similarity;
and determining the ranking value of the video to be recommended according to the predicted value, the weight corresponding to the predicted value, the similarity and the weight corresponding to the similarity.
For the above method, in one possible implementation, the label includes multiple layers.
For the above method, in a possible implementation manner, calculating the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended includes:
and calculating the similarity of the label corresponding to the user and the label corresponding to the video to be recommended on the same layer.
For the above method, in a possible implementation manner, after calculating the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended, the method further includes:
and under the condition that the similarity is greater than a first preset value and less than 1, adding a label corresponding to the user to the video to be recommended, wherein the first preset value is greater than or equal to 0 and less than 1.
In order to solve the above technical problem, according to another embodiment of the present disclosure, there is provided a video recommendation apparatus including:
the first label determining module is used for determining a label corresponding to a video currently watched by a user;
the video to be recommended determining module is used for determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos;
the sorting module is used for sorting the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sorting result;
and the video recommending module is used for recommending the videos to be recommended according to the sequencing result.
For the apparatus, in a possible implementation manner, the sorting module includes:
a first ranking value determining submodule, configured to determine, for each video to be recommended, a ranking value of the video to be recommended according to a tag corresponding to the currently-viewed video, a tag corresponding to the video to be recommended, and a tag corresponding to the user;
and the sequencing submodule is used for sequencing the videos to be recommended according to the sequencing values to obtain a sequencing result.
For the apparatus, in a possible implementation manner, the first ordering value determining sub-module includes:
a predicted value determining submodule, configured to determine, for each video to be recommended, a predicted value of the video to be recommended according to a tag corresponding to the currently viewed video and a tag corresponding to the video to be recommended, respectively;
and the second ordering value determining submodule is used for adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ordering value of the video to be recommended.
For the apparatus, in a possible implementation manner, the second sorting value determining sub-module includes:
the similarity operator module is used for calculating the similarity between the label corresponding to the user and the label corresponding to the video to be recommended;
and the third ordering value determining submodule is used for adjusting the predicted value of the video to be recommended according to the similarity to obtain the ordering value of the video to be recommended.
For the apparatus, in a possible implementation manner, the third sorting value determining sub-module includes:
the weight determining submodule is used for determining the weight corresponding to the predicted value and the weight corresponding to the similarity;
and the fourth ranking value determining submodule is used for determining the ranking value of the video to be recommended according to the predicted value, the weight corresponding to the predicted value, the similarity and the weight corresponding to the similarity.
For the above apparatus, in one possible implementation, the label includes multiple layers.
For the above apparatus, in one possible implementation, the similarity operator module includes:
and the similarity calculation operator module of the same layer is used for calculating the similarity of the label corresponding to the user and the label corresponding to the video to be recommended on the same layer.
For the above apparatus, in one possible implementation manner, the apparatus further includes:
and the label adding module is used for adding a label corresponding to the user to the video to be recommended under the condition that the similarity is greater than a first preset value and less than 1, wherein the first preset value is greater than or equal to 0 and less than 1.
In order to solve the above technical problem, according to another embodiment of the present disclosure, there is provided a video recommendation apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
determining a label corresponding to a video currently watched by a user;
determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos;
sequencing the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sequencing result;
and recommending the videos to be recommended according to the sequencing result.
Advantageous effects
The method comprises the steps of determining a label corresponding to a video currently watched by a user, determining a plurality of videos to be recommended according to the label corresponding to the video currently watched, sequencing the plurality of videos to be recommended according to the label corresponding to the video currently watched, the labels corresponding to the plurality of videos to be recommended and the label corresponding to the user to obtain a sequencing result, and recommending the plurality of videos to be recommended according to the sequencing result, so that the label corresponding to the user can be considered when the video related to the video currently watched by the user is recommended, and the video recommendation effect can be improved.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of an implementation of a video recommendation method according to an embodiment of the present disclosure.
Fig. 2 shows a flowchart of an exemplary implementation of step S13 of the video recommendation method according to an embodiment of the present disclosure.
Fig. 3 shows a flowchart of an exemplary implementation of step S21 of the video recommendation method according to an embodiment of the present disclosure.
Fig. 4 shows a flowchart of an exemplary implementation of step S32 of the video recommendation method according to an embodiment of the present disclosure.
Fig. 5 shows a flowchart of an exemplary implementation of step S42 of the video recommendation method according to an embodiment of the present disclosure.
Fig. 6 shows a block diagram of a video recommendation apparatus according to another embodiment of the present disclosure.
Fig. 7 is a block diagram illustrating an exemplary structure of a video recommendation apparatus according to another embodiment of the present disclosure.
Fig. 8 is a block diagram illustrating an apparatus 1900 for video recommendation, according to an example embodiment.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Example 1
Fig. 1 shows a flowchart of an implementation of a video recommendation method according to an embodiment of the present disclosure. The method can be applied to a server. As shown in fig. 1, the method includes:
in step S11, the tag corresponding to the video currently viewed by the user is determined.
As an example of this embodiment, the tag corresponding to the video may be determined according to at least one of the following: the method comprises the following steps of setting a title of a video, a label set for the video by a video uploader, a user image of the video uploader, a type of the video, main content of the video and comment content of the video.
In one possible implementation, the tag includes multiple layers.
As one example of this implementation, the labels may include a first layer of labels, a second layer of labels, and a third layer of labels. For example, word segmentation processing may be performed on a title of a video to obtain a third layer of tags corresponding to the video; abstracting a third layer of labels corresponding to the video to obtain second layer of labels corresponding to the video; abstracting the second layer of labels corresponding to the video to obtain the first layer of labels corresponding to the video. For example, a video is titled "Yi En Bu sufficient" fake boy "addiction" in < coffee prince number 1 shop. Performing word segmentation on the title to obtain three third-layer tags corresponding to the title: "coffee prince 1 shop", "Yi En Hui" and "fake boy". Abstracting the third layer label 'coffee prince 1 shop' to obtain a second layer label 'drama' corresponding to the third layer label 'coffee prince 1 shop', abstracting the second layer label 'drama', and obtaining a first layer label 'drama' corresponding to the second layer label 'drama'. The third layer label "Yi En Hui" is abstracted, the second layer label "Korean star" corresponding to the third layer label "Yi En Hui" can be obtained, and the first layer label "entertainment star" corresponding to the second layer label "Korean star" can be obtained by abstracting the second layer label "Korean star". The third layer label 'false child' is abstracted, so that the second layer label 'character characteristic nouns' corresponding to the third layer label 'false child' can be obtained, and the first layer label 'abstract nouns' corresponding to the second layer label 'character characteristic nouns' can be obtained by abstracting the second layer label 'character characteristic nouns'.
In addition, according to the title of the video and the label set for the video by the video uploader, the second layer label or the first layer label corresponding to the video may also be directly obtained, which is not limited herein.
It should be noted that although the multi-layer label is described above by taking the label including the first layer label, the second layer label and the third layer label as an example, those skilled in the art will appreciate that the present disclosure should not be limited thereto. In fact, the skilled person is fully capable of flexibly setting the number of layers of the label according to personal preferences and/or actual application scenarios. For example, the number of labels, the storage magnitude corresponding to the data added by the sample, and the time cost of model training can be comprehensively considered to determine that several layers of labels need to be set.
In step S12, a plurality of videos to be recommended are determined according to the tags corresponding to the currently viewed videos.
As an example of this embodiment, a plurality of videos to be recommended may be screened from the video library to be selected according to the label corresponding to the currently viewed video.
In step S13, the videos to be recommended are sorted according to the label corresponding to the currently viewed video, the labels corresponding to the videos to be recommended, and the label corresponding to the user, so as to obtain a sorting result.
Wherein, the label corresponding to the user can be determined according to at least one of the following items: the method comprises the following steps of setting a title of a video watched by a user, a label set for the video by a video uploader in the video watched by the user, a type of the video watched by the user, a user portrait of the video uploader in the video watched by the user, main content of the video watched by the user, comment content of the video watched by the user and video related information corresponding to payment behaviors of the user.
For example, a certain user often watches a korean drama, often watches a drama episode of one or several korean starring actors, or often watches a work of a certain drama. The statistical processing is carried out on the watching behavior data of the user, so that the label corresponding to the user can be obtained, and the labels of different layers corresponding to the user can be obtained.
It should be noted that, a person skilled in the art may determine, according to a requirement, a tag corresponding to a currently viewed video, a tag corresponding to a video to be recommended, and a tag corresponding to a user, which are specifically determined according to which data, and this embodiment does not limit this.
In step S14, a plurality of videos to be recommended are recommended according to the sorting result.
As an example of this embodiment, N videos to be recommended that are ranked first in the ranking result may be recommended, where N is a positive integer and is less than or equal to the total number of videos to be recommended.
As an example of the present embodiment, the recommended video may be displayed on any one or more of the right side, the lower side, the upper side, and the left side of the video play window.
According to the method and the device, when the video related to the video currently watched by the user is recommended, the label corresponding to the user is considered, namely the user related information is considered, so that personalized recommendation can be realized for the user, and the video recommendation effect can be improved.
According to the embodiment, in a possible implementation manner, the videos to be recommended and the ranking results of the videos to be recommended may be determined only according to the tags corresponding to the videos currently watched by the user and the tags corresponding to the user.
According to this embodiment, in another possible implementation manner, a tag corresponding to a video currently watched by a user, a tag corresponding to the user, and video related information such as keywords, types, scores, and the like of the video currently watched by the user may be combined to determine a video to be recommended and a ranking result of the video to be recommended. For example, a label corresponding to a video currently watched by a user and a label corresponding to the user may be added to a feature set of an existing model to enrich features corresponding to the model, so as to improve the video recommendation effect. The existing model may be a video recommendation model in the related art, and is not limited herein.
According to the embodiment, in another possible implementation manner, after the videos to be recommended and the sorting results of the videos to be recommended are determined through an existing model, secondary sorting can be performed on the videos to be recommended according to the tags corresponding to the videos currently watched by the user and the tags corresponding to the users, so that the video recommendation effect is improved.
Fig. 2 shows a flowchart of an exemplary implementation of step S13 of the video recommendation method according to an embodiment of the present disclosure. As shown in fig. 2, sorting the videos to be recommended according to the tags corresponding to the currently viewed videos, the tags corresponding to the videos to be recommended, and the tags corresponding to the users to obtain a sorting result, including:
in step S21, for each video to be recommended, the ranking value of the video to be recommended is determined according to the label corresponding to the currently viewed video, the label corresponding to the video to be recommended, and the label corresponding to the user.
The larger the ranking value of a certain video to be recommended is, the more likely the user watches the video to be recommended is.
In step S22, the videos to be recommended are sorted according to the sorting value, and a sorting result is obtained.
For example, the videos to be recommended may be sorted in the descending order of the sorting values to obtain a sorting result.
As an example of this embodiment, all videos to be recommended may be sorted in an off-line manner according to a descending order of the sorting values to obtain a sorting result, and the sorting result may be updated to an on-line storage environment.
As another example of this embodiment, real-time ranking calculation may be performed in an online manner, and all videos to be recommended are ranked in the descending order of ranking values to obtain a ranking result.
Fig. 3 shows a flowchart of an exemplary implementation of step S21 of the video recommendation method according to an embodiment of the present disclosure. As shown in fig. 3, for each video to be recommended, determining a ranking value of the video to be recommended according to a tag corresponding to a currently viewed video, a tag corresponding to the video to be recommended, and a tag corresponding to a user respectively includes:
in step S31, for each video to be recommended, a predicted value of the video to be recommended is determined according to the label corresponding to the currently viewed video and the label corresponding to the video to be recommended, respectively.
In this example, for each video to be recommended, a predicted value of the video to be recommended is determined according to a tag corresponding to a currently viewed video and a tag corresponding to the video to be recommended. For example, the similarity between the tag corresponding to the currently viewed video and the tag corresponding to the video to be recommended may be calculated, and the prediction value of the video to be recommended may be determined according to the similarity. In other words, the greater the similarity between the tag corresponding to the currently viewed video and the tag corresponding to the video to be recommended, the greater the predicted value of the video to be recommended.
It should be noted that, a person skilled in the art may use various methods to calculate the similarity between the tag corresponding to the currently viewed video and the tag corresponding to the video to be recommended, and this example does not limit this.
In step S32, the predicted value of the video to be recommended is adjusted according to the label corresponding to the user, so as to obtain the ranking value of the video to be recommended.
In this example, after the predicted value of the video to be recommended is determined according to the tag corresponding to the currently viewed video and the tag corresponding to the video to be recommended, the predicted value of the video to be recommended is adjusted according to the tag corresponding to the user.
Because the preference degrees and more inclined ranking results of different users for the videos to be recommended in the same batch are likely to be different. Therefore, in this example, the predicted values of the videos to be recommended are adjusted according to the tags corresponding to the users, and therefore the tags corresponding to the users are used to intervene in the ranking of the videos to be recommended, so that different ranking results are obtained for different users, personalized recommendation of the videos is achieved, user experience can be improved, and video recommendation effects can be improved.
In one possible implementation, the method may further include: and determining the weight of each label corresponding to the user. For example, if a video watched by a certain user is a drama, and there are many dramas, the ratio of the number of videos watched by the user to the total number of videos watched by the user may be used as the weight corresponding to the label "drama". In the implementation mode, each label corresponding to the user has weight information, the predicted value of the video to be recommended is adjusted according to each label corresponding to the user and the weight of each label corresponding to the user, the ranking value of the video to be recommended can be further optimized, and therefore the video recommendation effect is further improved.
It should be noted that, when training the model according to the label corresponding to the currently viewed video, the labels corresponding to the multiple videos to be recommended, and the label corresponding to the user, the label may be described by using a metric value [0,1], or the label may be described by using a non-metric value. The label is described by using the metric value [0,1], and a characteristic value corresponding to the label is determined in the interval [0,1] according to the weight of the label, wherein the weight of the label can be obtained through model training. Describing the label by using the non-metric value can be that when the video or the user does not include the label, the characteristic value of the label corresponding to the video or the user is 0; when the video or the user includes the tag, the feature value of the tag corresponding to the video or the user is 1. In addition, the model training may be performed in an off-line manner or in an on-line manner, which is not limited herein.
Fig. 4 shows a flowchart of an exemplary implementation of step S32 of the video recommendation method according to an embodiment of the present disclosure. As shown in fig. 4, adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ranking value of the video to be recommended, includes:
in step S41, the similarity between the label corresponding to the user and the label corresponding to the video to be recommended is calculated.
It should be noted that, those skilled in the art may use various methods to calculate the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended, and this example does not limit this.
In a possible implementation manner, calculating the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended includes: and calculating the similarity of the label corresponding to the user and the label corresponding to the video to be recommended on the same layer. For example, if a label A3 of a certain video to be recommended is a third-layer label and a label B2 corresponding to the user is a second-layer label, a second-layer label a2 corresponding to the third-layer label A3 may be determined first, then the similarity between the second-layer label a2 and the second-layer label B2 may be calculated, and the similarity between the second-layer label a2 and the second-layer label B2 may be determined as the similarity between the third-layer label A3 and the second-layer label B2.
In step S42, the predicted value of the video to be recommended is adjusted according to the similarity, so as to obtain the ranking value of the video to be recommended.
In a possible implementation manner, after calculating the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended, the method further includes: and under the condition that the similarity is greater than a first preset value and less than 1, adding a label corresponding to the user to the video to be recommended, wherein the first preset value is greater than or equal to 0 and less than 1. In this implementation manner, if the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended is greater than the first preset value and less than 1, it may be shown that the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended is higher, and the tag corresponding to the user is different from the tag corresponding to the video to be recommended.
Fig. 5 shows a flowchart of an exemplary implementation of step S42 of the video recommendation method according to an embodiment of the present disclosure. As shown in fig. 5, adjusting the predicted value of the video to be recommended according to the similarity to obtain the ranking value of the video to be recommended, includes:
in step S51, a weight corresponding to the predicted value and a weight corresponding to the similarity are determined.
In this example, when the predicted value of the video to be recommended is adjusted according to the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended, a weight corresponding to the predicted value and a weight corresponding to the similarity need to be determined. It should be noted that, a person skilled in the art may set the size of the weight corresponding to the predicted value and the size of the weight corresponding to the similarity according to the actual requirement of the application scenario. The larger the weight corresponding to the similarity is, the larger the influence of the label corresponding to the user on the ranking of the videos to be recommended is.
In step S52, the ranking value of the video to be recommended is determined according to the predicted value, the weight corresponding to the predicted value, the similarity, and the weight corresponding to the similarity.
For example, S ═ Q1×R+Q2X C, wherein S represents to be pushedRank value of recommended video, R represents predicted value, Q1Representing the weight corresponding to the predicted value, C representing the similarity, Q2And representing the weight corresponding to the similarity.
Example 2
Fig. 6 shows a block diagram of a video recommendation apparatus according to another embodiment of the present disclosure. The apparatus shown in fig. 6 may be used to run the video recommendation method shown in fig. 1 to 5. For convenience of explanation, only a part related to the present embodiment is shown in fig. 6.
As shown in fig. 6, the apparatus includes: a first tag determining module 61, configured to determine a tag corresponding to a video currently watched by a user; a to-be-recommended video determining module 62, configured to determine multiple to-be-recommended videos according to tags corresponding to currently viewed videos; the sorting module 63 is configured to sort the multiple videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the multiple videos to be recommended, and the tags corresponding to the users, so as to obtain a sorting result; and the video recommending module 64 is configured to recommend the videos to be recommended according to the sorting result.
Fig. 7 is a block diagram illustrating an exemplary structure of a video recommendation apparatus according to another embodiment of the present disclosure. The apparatus shown in fig. 7 may be used to run the video recommendation method shown in fig. 1 to 5. For convenience of explanation, only a part related to the present embodiment is shown in fig. 7. Components in fig. 7 that are numbered the same as those in fig. 6 have the same functions, and detailed descriptions of these components are omitted for the sake of brevity. As shown in fig. 7:
in one possible implementation, the sorting module 63 includes: the first ranking value determining sub-module 631 is configured to determine, for each video to be recommended, a ranking value of the video to be recommended according to a tag corresponding to a currently watched video, a tag corresponding to the video to be recommended, and a tag corresponding to a user; the sorting submodule 632 is configured to sort the multiple videos to be recommended according to the sorting value, so as to obtain a sorting result.
In one possible implementation, the first ordering value determining sub-module 631 includes: the prediction value determining submodule is used for determining the prediction value of the video to be recommended according to the label corresponding to the currently watched video and the label corresponding to the video to be recommended for each video to be recommended; and the second ordering value determining submodule is used for adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ordering value of the video to be recommended.
In one possible implementation, the second ordering value determining sub-module includes: the similarity calculation operator module is used for calculating the similarity between the label corresponding to the user and the label corresponding to the video to be recommended; and the third ordering value determining submodule is used for adjusting the predicted value of the video to be recommended according to the similarity to obtain the ordering value of the video to be recommended.
In one possible implementation, the third ordering value determining sub-module includes: the weight determining submodule is used for determining the weight corresponding to the predicted value and the weight corresponding to the similarity; and the fourth ordering value determining submodule is used for determining the ordering value of the video to be recommended according to the predicted value, the weight corresponding to the predicted value, the similarity and the weight corresponding to the similarity.
In one possible implementation, the tag includes multiple layers.
In one possible implementation, the similarity operator module includes: and the similarity calculation operator module of the same layer is used for calculating the similarity of the label corresponding to the user and the label corresponding to the video to be recommended on the same layer.
In one possible implementation, the apparatus further includes: the tag adding module 65 is configured to add a tag corresponding to the user to the video to be recommended when the similarity is greater than a first preset value and less than 1, where the first preset value is greater than or equal to 0 and less than 1.
The method comprises the steps of determining a label corresponding to a video currently watched by a user, determining a plurality of videos to be recommended according to the label corresponding to the video currently watched, sequencing the plurality of videos to be recommended according to the label corresponding to the video currently watched, the labels corresponding to the plurality of videos to be recommended and the label corresponding to the user to obtain a sequencing result, and recommending the plurality of videos to be recommended according to the sequencing result, so that the label corresponding to the user can be considered when the video related to the video currently watched by the user is recommended, and the video recommendation effect can be improved.
Example 3
Fig. 8 is a block diagram illustrating an apparatus 1900 for video recommendation, according to an example embodiment. For example, the apparatus 1900 may be provided as a server. Referring to FIG. 8, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the video recommendation method described above.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided that includes instructions, such as the memory 1932 that includes instructions, which are executable by the processing component 1922 of the apparatus 1900 to perform the above-described method.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (15)
1. A method for video recommendation, comprising:
determining tags corresponding to a video currently watched by a user, wherein the tags comprise multilayer tags, and the multilayer tags comprise a third layer of tags determined according to video information, a second layer of tags obtained by abstracting the third layer of tags, and a first layer of tags obtained by abstracting the second layer of tags;
determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos;
sequencing the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sequencing result;
recommending the videos to be recommended according to the sequencing result,
wherein the method further comprises:
after the similarity between the label corresponding to the user and the label corresponding to the video to be recommended is obtained, adding the label corresponding to the user to the video to be recommended under the condition that the similarity is greater than a first preset value and less than 1, wherein the first preset value is greater than or equal to 0 and less than 1.
2. The method according to claim 1, wherein the step of sorting the videos to be recommended according to the tags corresponding to the currently viewed videos, the tags corresponding to the videos to be recommended, and the tags corresponding to the user to obtain a sorting result comprises:
for each video to be recommended, determining a ranking value of the video to be recommended according to the label corresponding to the currently watched video, the label corresponding to the video to be recommended and the label corresponding to the user;
and sequencing the videos to be recommended according to the sequencing value to obtain a sequencing result.
3. The method according to claim 2, wherein for each video to be recommended, determining a ranking value of the video to be recommended according to the label corresponding to the currently viewed video, the label corresponding to the video to be recommended, and the label corresponding to the user respectively comprises:
for each video to be recommended, determining a predicted value of the video to be recommended according to the label corresponding to the currently watched video and the label corresponding to the video to be recommended respectively;
and adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ranking value of the video to be recommended.
4. The method according to claim 3, wherein adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ranking value of the video to be recommended comprises:
calculating the similarity between the label corresponding to the user and the label corresponding to the video to be recommended;
and adjusting the predicted value of the video to be recommended according to the similarity to obtain the ranking value of the video to be recommended.
5. The method according to claim 4, wherein adjusting the predicted value of the video to be recommended according to the similarity to obtain the ranking value of the video to be recommended comprises:
determining the weight corresponding to the predicted value and the weight corresponding to the similarity;
and determining the ranking value of the video to be recommended according to the predicted value, the weight corresponding to the predicted value, the similarity and the weight corresponding to the similarity.
6. The method of any one of claims 1 to 5, wherein the label comprises multiple layers.
7. The method according to claim 4, wherein calculating the similarity between the label corresponding to the user and the label corresponding to the video to be recommended comprises:
and calculating the similarity of the label corresponding to the user and the label corresponding to the video to be recommended on the same layer.
8. A video recommendation apparatus, comprising:
the first tag determining module is used for determining tags corresponding to a video currently watched by a user, wherein the tags comprise multilayer tags, and the multilayer tags comprise a third layer of tags determined according to video information, a second layer of tags obtained by abstracting the third layer of tags, and a first layer of tags obtained by abstracting the second layer of tags;
the video to be recommended determining module is used for determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos;
the sorting module is used for sorting the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sorting result;
a video recommending module for recommending a plurality of videos to be recommended according to the sorting result,
wherein the apparatus further comprises:
and the tag adding module is used for adding the tag corresponding to the user to the video to be recommended under the condition that the similarity is greater than a first preset value and less than 1 after the similarity between the tag corresponding to the user and the tag corresponding to the video to be recommended is obtained, wherein the first preset value is greater than or equal to 0 and less than 1.
9. The apparatus of claim 8, wherein the ordering module comprises:
a first ranking value determining submodule, configured to determine, for each video to be recommended, a ranking value of the video to be recommended according to a tag corresponding to the currently-viewed video, a tag corresponding to the video to be recommended, and a tag corresponding to the user;
and the sequencing submodule is used for sequencing the videos to be recommended according to the sequencing values to obtain a sequencing result.
10. The apparatus of claim 9, wherein the first ordering value determination submodule comprises:
a predicted value determining submodule, configured to determine, for each video to be recommended, a predicted value of the video to be recommended according to a tag corresponding to the currently viewed video and a tag corresponding to the video to be recommended, respectively;
and the second ordering value determining submodule is used for adjusting the predicted value of the video to be recommended according to the label corresponding to the user to obtain the ordering value of the video to be recommended.
11. The apparatus of claim 10, wherein the second rank value determination submodule comprises:
the similarity operator module is used for calculating the similarity between the label corresponding to the user and the label corresponding to the video to be recommended;
and the third ordering value determining submodule is used for adjusting the predicted value of the video to be recommended according to the similarity to obtain the ordering value of the video to be recommended.
12. The apparatus of claim 11, wherein the third rank value determination submodule comprises:
the weight determining submodule is used for determining the weight corresponding to the predicted value and the weight corresponding to the similarity;
and the fourth ranking value determining submodule is used for determining the ranking value of the video to be recommended according to the predicted value, the weight corresponding to the predicted value, the similarity and the weight corresponding to the similarity.
13. The apparatus of any one of claims 8 to 12, wherein the label comprises a plurality of layers.
14. The apparatus of claim 11, wherein the similarity operator module comprises:
and the similarity calculation operator module of the same layer is used for calculating the similarity of the label corresponding to the user and the label corresponding to the video to be recommended on the same layer.
15. A video recommendation apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
determining tags corresponding to a video currently watched by a user, wherein the tags comprise multilayer tags, and the multilayer tags comprise a third layer of tags determined according to video information, a second layer of tags obtained by abstracting the third layer of tags, and a first layer of tags obtained by abstracting the second layer of tags;
determining a plurality of videos to be recommended according to the labels corresponding to the currently watched videos;
sequencing the videos to be recommended according to the tags corresponding to the currently watched videos, the tags corresponding to the videos to be recommended and the tags corresponding to the users to obtain a sequencing result;
recommending the videos to be recommended according to the sequencing result,
wherein the processor is further configured to:
after the similarity between the label corresponding to the user and the label corresponding to the video to be recommended is obtained, adding the label corresponding to the user to the video to be recommended under the condition that the similarity is greater than a first preset value and less than 1, wherein the first preset value is greater than or equal to 0 and less than 1.
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| CN108563670B (en) * | 2018-01-12 | 2021-04-27 | 武汉斗鱼网络科技有限公司 | Video recommendation method and device, server and computer-readable storage medium |
| CN108228911A (en) * | 2018-02-11 | 2018-06-29 | 北京搜狐新媒体信息技术有限公司 | The computational methods and device of a kind of similar video |
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| CN110555157B (en) * | 2018-03-27 | 2023-04-07 | 阿里巴巴（中国）有限公司 | Content recommendation method, content recommendation device and electronic equipment |
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