CN117155997A - Multi-screen interactive content recommendation method, device, equipment and storage medium - Google Patents
Multi-screen interactive content recommendation method, device, equipment and storage medium Download PDFInfo
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- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
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Abstract
The invention relates to the technical field of multi-screen interaction, and discloses a multi-screen interaction content recommendation method, device, equipment and storage medium, wherein the method comprises the following steps: when a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream fragment corresponding to the multi-screen interaction request, sending the video stream fragment to a family member of a second authority level, wherein the second authority level is higher than the first authority level, responding to a recommendation instruction fed back by the family member of the second authority level, and recommending contents according to the recommendation instruction; the content recommendation method and the device based on the multi-screen interaction and the permission level can fully consider the requirements of new scenes of the interactive network television, and further can provide better multi-screen interaction experience for users.
Description
Technical Field
The present invention relates to the field of multi-screen interaction technologies, and in particular, to a multi-screen interaction content recommendation method, device, equipment, and storage medium.
Background
Currently, an interactive internet protocol television (Internet Protocol Television, IPTV) provides high-quality services such as Virtual Reality (VR) video, VR stadium, free view, multi-view, large and small screen interaction, and provides ultra-high definition, immersive, personalized and autonomous brand new experience for family members. Therefore, how to perform IPTV content recommendation in a multi-screen interaction scenario, so as to better meet the user requirements, is a technical problem to be solved urgently.
Disclosure of Invention
The invention mainly aims to provide a multi-screen interactive content recommendation method, a device, equipment and a storage medium, and aims to solve the technical problem of how to recommend IPTV content in a multi-screen interactive scene, and the technical problem of better meeting user requirements.
In order to achieve the above object, the present invention provides a multi-screen interactive content recommendation method, which includes:
when a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream fragment corresponding to the multi-screen interaction request and sending the video stream fragment to a family member of a second authority level, wherein the second authority level is higher than the first authority level;
and responding to a recommendation command fed back by the family member of the second permission level, and recommending the content according to the recommendation command.
Optionally, the recommendation instruction responding to the feedback of the family member of the second authority level, and recommending the content according to the recommendation instruction, includes:
a recommendation instruction fed back by the family member of the second authority level is responded;
generating a content label and calculating content label weight corresponding to the content label;
generating interest labels of family members, and calculating interest label weights corresponding to the interest labels;
calculating family member similarity among family members;
calculating the similarity of content attributes among the contents;
and recommending the content according to the content tag weight, the interest tag weight, the family member similarity and the content attribute similarity.
Optionally, the generating a content tag and calculating a content tag weight corresponding to the content tag include:
generating content tags, and counting the total number of recommended content and the total recommended times of the content tags;
detecting whether the content category to which the content tag belongs is marked by family members of a second authority level or not, and generating a correction factor according to a detection result;
and calculating the content tag weight corresponding to the content tags according to the total recommended content number, the total recommended content tag number and the correction factor.
Optionally, the generating the interest tag of the family member and calculating the interest tag weight corresponding to the interest tag include:
collecting family member data, and generating an interest tag set of family members according to the family member data;
acquiring the occurrence times of the interest tags in the interest tag set of the family member, and calculating the average value of the occurrence times;
calculating relationship affinity assignment according to the relationships among family members, and calculating a relationship affinity assignment average value;
and calculating the weight of the interest tag corresponding to the interest tag according to the occurrence times, the average value and the relationship affinity degree assignment average value.
Optionally, the calculating the family member similarity between family members includes:
acquiring an evaluation value of family members for content, and acquiring a grading average value of the family members for the content;
acquiring the evaluation time of family members on the content, and setting an attenuation factor according to the interest change condition of the family members;
and calculating family member similarity among family members according to the evaluation value, the evaluation average value, the evaluation time and the attenuation factor.
Optionally, the calculating the similarity of the content attributes between the contents includes:
extracting related texts of the content, and converting the related texts into text vectors;
and calculating the similarity of the content attributes among the contents according to the text vectors through a text similarity algorithm.
Optionally, the multi-screen interactive content recommendation method further includes:
acquiring multi-screen interaction data of family members;
and determining the authority level of the family member according to the multi-screen interaction data, wherein the authority level comprises a first authority level and a second authority level.
In addition, in order to achieve the above object, the present invention further provides a multi-screen interactive content recommendation device, which includes:
the sending module is used for intercepting a video stream fragment corresponding to a multi-screen interaction request to be sent to a family member with a second authority level when the family member with the first authority level initiates the multi-screen interaction request, wherein the second authority level is higher than the first authority level;
and the recommending module is used for responding to the recommending instruction fed back by the family member of the second authority level and recommending the content according to the recommending instruction.
In addition, in order to achieve the above object, the present invention also provides a multi-screen interactive content recommendation device, which includes a memory, a processor, and a multi-screen interactive content recommendation program stored on the memory and executable on the processor, wherein the multi-screen interactive content recommendation program is configured to implement the multi-screen interactive content recommendation method as described above.
In addition, in order to achieve the above object, the present invention further provides a storage medium, on which a multi-screen interactive content recommendation program is stored, which when executed by a processor, implements the multi-screen interactive content recommendation method as described above.
When a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream fragment corresponding to the multi-screen interaction request, sending the video stream fragment to a family member of a second authority level, wherein the second authority level is higher than the first authority level, responding to a recommendation instruction fed back by the family member of the second authority level, and recommending contents according to the recommendation instruction; the content recommendation method and the device based on the multi-screen interaction and the permission level can fully consider the requirements of new scenes of the interactive network television, and further can provide better multi-screen interaction experience for users.
Drawings
FIG. 1 is a schematic structural diagram of a multi-screen interactive content recommendation device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a multi-screen interactive content recommendation method according to a first embodiment of the present invention;
FIG. 3 is an overall interaction diagram of an embodiment of a multi-screen interactive content recommendation method according to the present invention;
FIG. 4 is a flowchart illustrating a multi-screen interactive content recommendation method according to a second embodiment of the present invention;
FIG. 5 is a flowchart illustrating a multi-screen interactive content recommendation method according to a third embodiment of the present invention;
fig. 6 is a block diagram illustrating a structure of a multi-screen interactive contents recommendation apparatus according to a first embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a multi-screen interactive content recommendation device in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the multi-screen interactive content recommendation apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display (Display), and the optional user interface 1003 may also include a standard wired interface, a wireless interface, and the wired interface for the user interface 1003 may be a USB interface in the present invention. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the multi-screen interactive content recommendation device, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
As shown in FIG. 1, a memory 1005, which is considered to be a computer storage medium, may include an operating system, a network communication module, a user interface module, and a multi-screen interactive content recommendation program.
In the multi-screen interactive content recommendation device shown in fig. 1, the network interface 1004 is mainly used for connecting a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting user equipment; the multi-screen interactive content recommendation device invokes a multi-screen interactive content recommendation program stored in the memory 1005 through the processor 1001, and executes the multi-screen interactive content recommendation method provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the multi-screen interactive content recommendation method is provided.
Referring to fig. 2, fig. 2 is a flowchart of a first embodiment of a multi-screen interactive content recommendation method according to the present invention, and the first embodiment of the multi-screen interactive content recommendation method according to the present invention is provided.
Step S10: when a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream segment corresponding to the multi-screen interaction request and sending the video stream segment to a family member of a second authority level, wherein the second authority level is higher than the first authority level.
It should be understood that the execution body of the present embodiment may be a multi-screen interactive content recommendation device with functions of data processing, network communication and program running, for example, an IPTV terminal, or other electronic devices capable of implementing the same or similar functions, which is not limited in this embodiment, where the IPTV terminal includes, but is not limited to, an intelligent set top box, or the like.
It can be appreciated that the multi-screen interactive content recommendation method is applied to multi-screen interactive scenes, wherein the multi-screen interactive scenes comprise but are not limited to large-screen and small-screen interactive scenes, for example, each family member uses a small-screen terminal such as a mobile phone and the like to interact by connecting an IPTV terminal such as an intelligent set top box and the like with a large-screen television.
It should be understood that each family member has a permission level, which may be preset in the system of the IPTV terminal, or may acquire multi-screen interaction data of each family member terminal, and determine according to the multi-screen interaction data, which is not limited in this embodiment. Wherein the second permission level is higher than the first permission level, e.g., the second permission level is a high permission level and the first permission level is a low permission level.
Step S20: and responding to a recommendation command fed back by the family member of the second permission level, and recommending the content according to the recommendation command.
For ease of understanding, the description is given with reference to fig. 3, but the present solution is not limited thereto. FIG. 3 is an overall interaction diagram of an embodiment of a multi-screen interactive content recommendation method according to the present invention, wherein the multi-screen interactive content recommendation method comprises the following specific steps:
1. when the low-authority-level family members perform large-screen and small-screen interaction through IPTV, the current video stream fragment is intercepted and sent to the high-authority-level family member terminal;
when a certain family member starts the IPTV through a small screen terminal such as a mobile phone to perform large screen interaction, the IPTV acquires the identity information of the current family member, and the authority level of the current family member is determined through the corresponding relation between the identity information and the authority level. If the current family member is a low authority level, the IPTV terminal intercepts a video stream fragment in a preset time period and sends the video stream fragment to the terminal of the family member with the high authority level.
The scene can be understood as that if children in family members play cloud games by using the IPTV terminal to connect with a large-screen television through a mobile phone, the IPTV terminal can send the video stream fragments of the games to the mobile phone of parents.
2. Content recommendation is performed in response to a recommendation instruction of a family member with a high authority level;
after the high-authority family members watch the video stream fragments, a recommendation button can be selected, and the IPTV terminal starts a recommendation program in response to the recommendation instruction.
The scene can be understood that when the family members with high authority level watch the video stream, the current game is considered to be unsuitable for the family members playing, and the IPTV terminal can start a recommendation program through a recommendation button to recommend suitable games for the current family members again for multi-screen interaction.
Of course, in order to improve the content recommendation efficiency and accuracy, in this embodiment, the server corresponding to the IPTV terminal may also perform content recommendation, and the IPTV terminal may issue the recommendation result, which is not limited in this embodiment.
In the embodiment, when a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream fragment corresponding to the multi-screen interaction request, sending the video stream fragment to a family member of a second authority level, wherein the second authority level is higher than the first authority level, responding to a recommendation instruction fed back by the family member of the second authority level, and recommending content according to the recommendation instruction; because the embodiment recommends the content based on the multi-screen interaction and the permission level, the requirement of the new scene of the interactive network television can be fully considered, and better multi-screen interaction experience can be provided for the user.
Referring to fig. 4, fig. 4 is a flowchart illustrating a second embodiment of the multi-screen interactive content recommendation method according to the present invention, and based on the first embodiment shown in fig. 2, the second embodiment of the multi-screen interactive content recommendation method according to the present invention is proposed.
In a second embodiment, the step S20 includes:
step S201: and responding to the recommended instruction fed back by the family member of the second authority level.
It should be understood that in this embodiment, when recommending to a user with a low authority level, the similarity of family members and the similarity of content attributes are considered, and the similarity is fused by combining the interest tag weight and the content tag weight, so that the accuracy of content recommendation is improved.
Step S202: and generating a content label and calculating a content label weight corresponding to the content label.
It can be understood that a content tag can be generated for each content, the tag can be a tag of the content, and text of the content profile can also be processed by adopting a natural language technology to obtain a plurality of tags of each content, so as to form a tag set. Taking video content as an example, the labels of video a may be: united states/scenario/love/comedy.
It should be understood that calculating the content tag weight corresponding to the content tag may be calculating the content tag weight corresponding to the content tag based on a preset content tag weight value algorithm, where the preset content tag weight value algorithm may be preset.
Further, in order to improve accuracy of the content tag weight, the step S202 includes:
generating content tags, and counting the total number of recommended content and the total recommended times of the content tags; detecting whether the content category to which the content tag belongs is marked by family members of a second authority level or not, and generating a correction factor according to a detection result; and calculating the content tag weight corresponding to the content tags according to the total recommended content number, the total recommended content tag number and the correction factor.
In one example, the weight w of each content tag is calculated based on the content category and the recommended number of content tags 1 The content categories are games, videos, education and the like, the recommended times of the tags are the recommended times of each tag in a preset time period, the recommended times are specifically reflected on the recommendation of the content, if a video A is recommended 5 times, 4 tags of the video are recorded 5 times in the United states/storyline/love/comedy, and the total recommended times of each content tag in the preset time period are counted. The specific formula is as follows:
wherein S is the total recommended number of the content, s″ is the total recommended number of the content tags, α is a correction factor, and its value is determined according to whether the content category to which the content tag belongs is marked by a high authority family member, if the content category is marked as favorite, the value of α is greater than 1, if the content category is marked as not favorite, less than 1, and not marked, equal to 1, and in the whole, the more the recommended number of the content tags is, the smaller the weight value is, otherwise, the less the recommended number of the content tags is, the higher the weight value is.
Step S203: generating interest labels of family members, and calculating the interest label weights corresponding to the interest labels.
It should be noted that, the labels of the family members are mainly interest labels, and the collected family member data may be IPTV collected, or data collected by smart home. The user authorization allows the user to send the interest tag to the IPTV terminal, or the interest tag of each family member can be obtained by calculating the smart home, and the interest tag is only sent to the IPTV terminal, which is not limited in the embodiment.
It may be understood that the calculating the interest tag weight corresponding to the interest tag may be calculating the interest tag weight corresponding to the interest tag by a preset interest tag weight value algorithm, where the preset interest tag weight value algorithm may be preset.
Further, in order to improve accuracy of the interest tag weight, the step S203 includes:
collecting family member data, and generating an interest tag set of family members according to the family member data; acquiring the occurrence times of the interest tags in the interest tag set of the family member, and calculating the average value of the occurrence times; calculating relationship affinity assignment according to the relationships among family members, and calculating a relationship affinity assignment average value; and calculating the weight of the interest tag corresponding to the interest tag according to the occurrence times, the average value and the relationship affinity degree assignment average value.
In one example, when a tag of interest appears in a tag set of only one family member, the tag weight W 2 =1. When a certain interest tag appears in the interest tag sets of a plurality of family members at the same time, then the weight W of each interest tag is calculated according to the relation among the family members 2 . Specifically, the relationship between family members is typically couple, father, son, mother, grandson, sibling, lover, friend, co-resident, etc., and can be classified into three levels of high, medium and low according to the intimacy degree, and respectively assigned with 10, 6 and 4. The specific calculation formula is as follows:
wherein a is the occurrence number of the interest tag in the interest tag set of the family member, a' is the average value of the occurrence number, C is the assignment average value of the relationship affinity degree, C 0 Is a preset threshold. And calculating a relationship affinity assignment average value C according to the relationship affinity assignment, wherein if the label game appears in a label set of four family members of the ABCD, the number of occurrences is 4, the relationship is 6, and the relationship assignment average value is an average value of 6 assignments.
Step S204: and calculating the similarity of family members among the family members.
It can be understood that calculating the similarity of family members between family members may be to obtain an evaluation value of the family members for the content and obtain an average value of scores of the family members for the content; acquiring the evaluation time of family members on the content, and setting an attenuation factor according to the interest change condition of the family members; and calculating family member similarity among family members according to the evaluation value, the evaluation average value, the evaluation time and the attenuation factor.
It should be appreciated that, considering that the score of a family member for a content may change over time, the recent evaluation value of the user for a certain content may reflect its recent interest, and thus, the influence of time on the similarity is combined in calculating the similarity of family members.
In one example, family member similarity is calculated by the following formula:
wherein R is ui 、R vi For the evaluation value of the content by users u and v,and +.>For average value of scores of users u and v for content, t u，xi For the time of evaluating a certain content by user u, t v，xi For the time when the user v evaluates a certain content, δ is an attenuation coefficient, when δ is larger, the interest of the family member changes faster, whereas when δ is smaller, the interest of the family member does not change much. The similarity among family members obtained by combining the attenuation coefficients can more accurately reflect the similarity among real users.
Step S205: and calculating the similarity of the content attributes among the contents.
It should be appreciated that calculating content attribute similarity between content may be extracting relevant text of the content and converting the relevant text into a text vector; and calculating the similarity of the content attributes among the contents according to the text vectors through a text similarity algorithm.
Firstly, extracting relevant texts of the content, for example, extracting key frames aiming at video content, adopting an OCR algorithm to carry out text recognition on the key frames, and extracting keywords; or extracting keywords directly according to texts such as brief introduction and description of the content to obtain keyword sets of each content, analyzing the keyword sets of the content through TF-IDF algorithm, converting the keyword sets into text vectors, and calculating content attribute similarity sim between the content through text similarity algorithm such as cosine similarity algorithm 2 。
Step S206: and recommending the content according to the content tag weight, the interest tag weight, the family member similarity and the content attribute similarity.
In one example, the fused similarity is calculated by the following formula:
sim＝W 2 sim 1 +W 1 sim 2
wherein sim is the similarity after fusion, w 2 For the interest tag weight corresponding to the interest tag, sim 1 Similarity of family members, w 1 For content label weight, sim corresponding to content label 2 Is the similarity of content attributes.
And then, selecting the nearest neighbor of the target family member according to the fused similarity, predicting the evaluation value of the target family member on each item based on the evaluation values of all users in the nearest neighbor, and selecting the TOPN content as a content recommendation result.
In this embodiment, considering the similarity of family members and the similarity of content attributes, the fused similarity matrix can more accurately complete recommendation, and simultaneously, in the fusion process, the weight of the family member tag and the weight of the content tag are combined, so that the accuracy of content recommendation is further improved.
Referring to fig. 5, fig. 5 is a flowchart illustrating a third embodiment of a multi-screen interactive content recommendation method according to the present invention, and based on the above embodiments, a third embodiment of a multi-screen interactive content recommendation method according to the present invention is provided.
In a third embodiment, before the step S10, the method further includes:
step S01: and acquiring multi-screen interaction data of family members.
It should be understood that, in order to improve the reliability of the authority level, in this embodiment, the authority level of the family member is determined according to the multi-screen interaction data of the family member.
It should be noted that the multi-screen interaction data may be data that each family member uses the IPTV terminal to perform multi-screen interaction in a preset time, where the preset time may be preset.
Step S02: and determining the authority level of the family member according to the multi-screen interaction data, wherein the authority level comprises a first authority level and a second authority level.
In one example, data of multi-screen interaction of each family member using IPTV in a period of time in the past may be obtained, and the permission level may be determined according to a content category and a data object, where the content category may be a game, an audio-visual, an education, etc., and the audio-visual category interaction number is that a higher family member has a higher permission, and in addition, the permission level of the family member may be determined according to age requirement data (i.e., a data object) of the game itself for the game category interaction. If the game is also multi-screen interaction of the game category, the game of the user A is 18+, and the game of the user B is 4+, the authority level of the user A is higher than that of the user B.
In the embodiment, the authority level of the family member is determined according to the multi-screen interaction data of the family member, so that the reliability of the authority level can be improved.
In addition, referring to fig. 6, an embodiment of the present invention further provides a multi-screen interactive content recommendation device, where the multi-screen interactive content recommendation device includes:
the sending module 10 is configured to intercept a video stream segment corresponding to a multi-screen interaction request and send the video stream segment to a family member with a second permission level when the family member with the first permission level initiates the multi-screen interaction request, where the second permission level is higher than the first permission level.
It can be appreciated that the multi-screen interactive content recommendation device is applied to multi-screen interactive scenes, wherein the multi-screen interactive scenes include but are not limited to large-screen and small-screen interactive scenes, for example, each family member uses a small-screen terminal such as a mobile phone and the like to interact through an IPTV terminal such as an intelligent set top box and the like connected with a large-screen television.
It should be understood that each family member has a permission level, which may be preset in the system of the IPTV terminal, or may acquire multi-screen interaction data of each family member terminal, and determine according to the multi-screen interaction data, which is not limited in this embodiment. Wherein the second permission level is higher than the first permission level, e.g., the second permission level is a high permission level and the first permission level is a low permission level.
And the recommendation module 20 is used for responding to the recommendation command fed back by the family member of the second permission level and recommending the content according to the recommendation command.
For ease of understanding, the description is given with reference to fig. 3, but the present solution is not limited thereto. FIG. 3 is an overall interaction diagram of an embodiment of a multi-screen interactive content recommendation method according to the present invention, wherein the multi-screen interactive content recommendation method comprises the following specific steps:
1. when the low-authority-level family members perform large-screen and small-screen interaction through IPTV, the current video stream fragment is intercepted and sent to the high-authority-level family member terminal;
when a certain family member starts the IPTV through a small screen terminal such as a mobile phone to perform large screen interaction, the IPTV acquires the identity information of the current family member, and the authority level of the current family member is determined through the corresponding relation between the identity information and the authority level. If the current family member is a low authority level, the IPTV terminal intercepts a video stream fragment in a preset time period and sends the video stream fragment to the terminal of the family member with the high authority level.
The scene can be understood as that if children in family members play cloud games by using the IPTV terminal to connect with a large-screen television through a mobile phone, the IPTV terminal can send the video stream fragments of the games to the mobile phone of parents.
2. Content recommendation is performed in response to a recommendation instruction of a family member with a high authority level;
after the high-authority family members watch the video stream fragments, a recommendation button can be selected, and the IPTV terminal starts a recommendation program in response to the recommendation instruction.
The scene can be understood that when the family members with high authority level watch the video stream, the current game is considered to be unsuitable for the family members playing, and the IPTV terminal can start a recommendation program through a recommendation button to recommend suitable games for the current family members again for multi-screen interaction.
Of course, in order to improve the content recommendation efficiency and accuracy, in this embodiment, the server corresponding to the IPTV terminal may also perform content recommendation, and the IPTV terminal may issue the recommendation result, which is not limited in this embodiment.
In the embodiment, when a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream fragment corresponding to the multi-screen interaction request, sending the video stream fragment to a family member of a second authority level, wherein the second authority level is higher than the first authority level, responding to a recommendation instruction fed back by the family member of the second authority level, and recommending content according to the recommendation instruction; because the embodiment recommends the content based on the multi-screen interaction and the permission level, the requirement of the new scene of the interactive network television can be fully considered, and better multi-screen interaction experience can be provided for the user.
In an embodiment, the recommendation module 20 is further configured to respond to a recommendation instruction fed back by the family member of the second permission level; generating a content label and calculating content label weight corresponding to the content label; generating interest labels of family members, and calculating interest label weights corresponding to the interest labels; calculating family member similarity among family members; calculating the similarity of content attributes among the contents; and recommending the content according to the content tag weight, the interest tag weight, the family member similarity and the content attribute similarity.
In an embodiment, the recommendation module 20 is further configured to generate content tags, and count the total number of recommended content and the total number of recommended content tags; detecting whether the content category to which the content tag belongs is marked by family members of a second authority level or not, and generating a correction factor according to a detection result; and calculating the content tag weight corresponding to the content tags according to the total recommended content number, the total recommended content tag number and the correction factor.
In an embodiment, the recommendation module 20 is further configured to collect family member data, and generate an interest tag set of a family member according to the family member data; acquiring the occurrence times of the interest tags in the interest tag set of the family member, and calculating the average value of the occurrence times; calculating relationship affinity assignment according to the relationships among family members, and calculating a relationship affinity assignment average value; and calculating the weight of the interest tag corresponding to the interest tag according to the occurrence times, the average value and the relationship affinity degree assignment average value.
In an embodiment, the recommendation module 20 is further configured to obtain an evaluation value of the family member for the content, and obtain an average value of scores of the family member for the content; acquiring the evaluation time of family members on the content, and setting an attenuation factor according to the interest change condition of the family members; and calculating family member similarity among family members according to the evaluation value, the evaluation average value, the evaluation time and the attenuation factor.
In one embodiment, the recommendation module 20 is further configured to extract related text of the content and convert the related text into a text vector; and calculating the similarity of the content attributes among the contents according to the text vectors through a text similarity algorithm.
In an embodiment, the multi-screen interactive content recommendation device further includes:
the grading module is used for acquiring multi-screen interaction data of family members; and determining the authority level of the family member according to the multi-screen interaction data, wherein the authority level comprises a first authority level and a second authority level.
Other embodiments or specific implementation manners of the multi-screen interactive content recommendation device of the present invention may refer to the above method embodiments, and are not described herein again.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a multi-screen interactive content recommendation program, and the multi-screen interactive content recommendation program realizes the multi-screen interactive content recommendation method when being executed by a processor.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read only memory mirror (Read Only Memory image, ROM)/random access memory (Random Access Memory, RAM), magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The multi-screen interactive content recommendation method is characterized by comprising the following steps of:
when a family member of a first authority level initiates a multi-screen interaction request, intercepting a video stream fragment corresponding to the multi-screen interaction request and sending the video stream fragment to a family member of a second authority level, wherein the second authority level is higher than the first authority level;
and responding to a recommendation command fed back by the family member of the second permission level, and recommending the content according to the recommendation command.
2. The multi-screen interactive content recommendation method according to claim 1, wherein the recommending instruction responding to the feedback of the family members of the second authority level and recommending the content according to the recommending instruction comprises:
a recommendation instruction fed back by the family member of the second authority level is responded;
generating a content label and calculating content label weight corresponding to the content label;
generating interest labels of family members, and calculating interest label weights corresponding to the interest labels;
calculating family member similarity among family members;
calculating the similarity of content attributes among the contents;
and recommending the content according to the content tag weight, the interest tag weight, the family member similarity and the content attribute similarity.
3. The multi-screen interactive content recommendation method according to claim 2, wherein the generating content tags and calculating content tag weights corresponding to the content tags comprises:
generating content tags, and counting the total number of recommended content and the total recommended times of the content tags;
detecting whether the content category to which the content tag belongs is marked by family members of a second authority level or not, and generating a correction factor according to a detection result;
and calculating the content tag weight corresponding to the content tags according to the total recommended content number, the total recommended content tag number and the correction factor.
4. The multi-screen interactive content recommendation method according to claim 2, wherein the generating the interest tag of the family member and calculating the interest tag weight corresponding to the interest tag comprises:
collecting family member data, and generating an interest tag set of family members according to the family member data;
acquiring the occurrence times of the interest tags in the interest tag set of the family member, and calculating the average value of the occurrence times;
calculating relationship affinity assignment according to the relationships among family members, and calculating a relationship affinity assignment average value;
and calculating the weight of the interest tag corresponding to the interest tag according to the occurrence times, the average value and the relationship affinity degree assignment average value.
5. The multi-screen interactive content recommendation method according to claim 2, wherein the calculating of family member similarity between family members comprises:
acquiring an evaluation value of family members for content, and acquiring a grading average value of the family members for the content;
acquiring the evaluation time of family members on the content, and setting an attenuation factor according to the interest change condition of the family members;
and calculating family member similarity among family members according to the evaluation value, the evaluation average value, the evaluation time and the attenuation factor.
6. The multi-screen interactive content recommendation method according to claim 2, wherein the calculating of the content attribute similarity between contents comprises:
extracting related texts of the content, and converting the related texts into text vectors;
and calculating the similarity of the content attributes among the contents according to the text vectors through a text similarity algorithm.
7. The multi-screen interactive content recommendation method according to any one of claims 1 to 6, further comprising:
acquiring multi-screen interaction data of family members;
and determining the authority level of the family member according to the multi-screen interaction data, wherein the authority level comprises a first authority level and a second authority level.
8. A multi-screen interactive content recommendation device, characterized in that the multi-screen interactive content recommendation device comprises:
the sending module is used for intercepting a video stream fragment corresponding to a multi-screen interaction request to be sent to a family member with a second authority level when the family member with the first authority level initiates the multi-screen interaction request, wherein the second authority level is higher than the first authority level;
and the recommending module is used for responding to the recommending instruction fed back by the family member of the second authority level and recommending the content according to the recommending instruction.
9. A multi-screen interactive content recommendation apparatus, characterized in that the multi-screen interactive content recommendation apparatus comprises: a memory, a processor and a multi-screen interactive content recommendation program stored on the memory and executable on the processor, which when executed by the processor implements the multi-screen interactive content recommendation method of any one of claims 1 to 7.
10. A storage medium having stored thereon a multi-screen interactive content recommendation program, which when executed by a processor implements the multi-screen interactive content recommendation method according to any one of claims 1 to 7.
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