CN114329230A - Information generation method and device - Google Patents
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- CN114329230A CN114329230A CN202111653689.4A CN202111653689A CN114329230A CN 114329230 A CN114329230 A CN 114329230A CN 202111653689 A CN202111653689 A CN 202111653689A CN 114329230 A CN114329230 A CN 114329230A
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Abstract
The disclosure provides an information generation method and device, and particularly relates to the technical field of information display. The specific implementation scheme is as follows: acquiring a first label set formed by content labels corresponding to recommended content; acquiring a second label set formed by attribute labels of praise users corresponding to the recommended content and the number of praise users corresponding to each label in the set; in the second label set, determining a target attribute label matched with the content label in the first label set; and generating praise information of the recommended content based on the target attribute label and the praise user number corresponding to the target attribute label. The mode is favorable for displaying crowd characteristic information behind praise information, and the efficiency of obtaining information by a user is effectively improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to an information generating method and apparatus.
Background
The praise data is a key factor for judging the content value of the user in the recommended scene, and directly influences the perception of the user on the content. In the current content platform, user approval data can often reflect satisfaction evaluation of a user on the content and is important for accurately recommending high-quality content; and meanwhile, the user can be assisted to quickly acquire required information by giving approval to the display of the data, and interaction and content consumption are efficiently carried out.
The method for displaying praise data in the prior art mainly comprises the following three methods: (1) and directly displaying the praise values. The scheme is used in a content platform with a user interaction module, directly shows the numerical value and reflects the general attraction of the content. (2) And displaying the praise values of the familiar people based on the user intimacy. (3) And showing the praise of the name of the user based on the intimacy of the user.
Disclosure of Invention
The embodiment of the disclosure provides an information generation method, an information generation device, information generation equipment and a storage medium.
In a first aspect, an embodiment of the present disclosure provides an information generating method, where the method includes: acquiring a first label set formed by content labels corresponding to recommended content; acquiring a second label set formed by attribute labels of praise users corresponding to the recommended content and the number of praise users corresponding to each label in the set; in the second label set, determining a target attribute label matched with the content label in the first label set; and generating praise information of the recommended content based on the target attribute label and the praise user number corresponding to the target attribute label.
In a second aspect, an embodiment of the present disclosure provides an information generating apparatus, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire a first label set formed by content labels corresponding to recommended content; the second acquisition module is configured to acquire a second label set formed by attribute labels of praise users corresponding to the recommended content and the number of praise users corresponding to each label in the set; the target determining module is configured to determine a target attribute label matched with the content label in the first label set in the second label set; and the information generation module is configured to generate praise information of the recommended content based on the target attribute tag and the praise user number corresponding to the target attribute tag.
In a third aspect, embodiments of the present disclosure provide an electronic device, which includes one or more processors; a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the information generating method as any one of the embodiments of the first aspect.
In a fourth aspect, the embodiments of the present disclosure provide a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements the information generating method as in any of the embodiments of the first aspect.
In a fifth aspect, the present disclosure provides a computer program product including a computer program, which when executed by a processor implements the information generating method according to any embodiment of the first aspect.
The method and the device effectively improve the efficiency of the user for acquiring the information.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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FIG. 1 is an exemplary system architecture diagram in which the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of an information generation method according to the present disclosure;
FIG. 3 is a schematic diagram of one application scenario of an information generation method according to the present disclosure;
FIG. 4 is a flow diagram of yet another embodiment of an information generation method according to the present disclosure;
FIG. 5 is a schematic diagram of one embodiment of an information generating device, according to the present disclosure;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the information generation methods of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen, including but not limited to a mobile phone and a notebook computer. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as a plurality of software or software modules (for example, for providing an information generating service), or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, for example, acquiring a first tag set composed of content tags corresponding to recommended content; acquiring a second label set formed by attribute labels of praise users corresponding to the recommended content and the number of praise users corresponding to each label in the set; in the second label set, determining a target attribute label matched with the content label in the first label set; and generating praise information of the recommended content based on the target attribute label and the praise user number corresponding to the target attribute label.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of pieces of software or software modules (for example, for providing an information generating service), or may be implemented as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the information generation method provided by the embodiment of the present disclosure may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103, or may be executed by the server 105 and the terminal devices 101, 102, and 103 in cooperation with each other. Accordingly, each part (for example, each unit, sub-unit, module, and sub-module) included in the information generating apparatus may be provided entirely in the server 105, entirely in the terminal devices 101, 102, and 103, or may be provided in the server 105 and the terminal devices 101, 102, and 103, respectively.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 shows a flow diagram 200 of an embodiment of an information generation method. The information generation method comprises the following steps:
In this embodiment, the executing entity (e.g., the server 105 or the terminal devices 101, 102, 103 in fig. 1) may acquire the first tag set composed of at least one content tag corresponding to the recommended content in a wired or wireless manner.
The recommended content may be any content, such as information, video, short video, live broadcast, and the like, pushed to the user in the community platform and to be read by the user, which is not limited in this application.
Here, the content tag corresponding to the recommended content may be generated based on a preset content understanding model, and the content understanding model may be obtained by training an initial content understanding model based on the recommended content sample labeled with the content tag.
The initial content understanding model may be various types of untrained or untrained artificial neural networks or a model obtained by combining various types of untrained or untrained artificial neural networks, for example, the initial content understanding model may be an untrained convolutional neural network, an untrained cyclic neural network, or a model obtained by combining an untrained convolutional neural network, an untrained cyclic neural network, and an untrained full connectivity layer.
The wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
It is noted that the content tags in the first set of tags are different from each other.
In this embodiment, the execution subject may obtain a second tag set formed by at least one attribute tag corresponding to each praise user in at least one praise user corresponding to the recommended content, and the number of praise users corresponding to each tag in the second tag set.
Here, the attribute tag of the user may be any attribute tag, for example, an interest attribute tag, a skill attribute tag, and the like, which is not limited in the present application.
Specifically, the execution subject may generate interest attribute tags of the users favored according to historical browsing information of two users favored corresponding to the recommended content, for example, the user favored a and the user favored B, where for example, the attribute tag (interest attribute) of the user favored a is an interest M, and the attribute tag (interest attribute) of the user favored B is an interest N, and further, according to the attribute tags of the users favored, generate a second tag set, that is, { interest M, interest N } and the number of users favored corresponding to each tag.
Here, each attribute label in the second set of labels is different.
In this embodiment, the execution subject may determine, in the second tag set, a target attribute tag that matches the content tag in the first tag set based on the text similarity.
One or more target attribute tags may be provided, which is not limited in this application.
Here, the executing entity may determine, in the second tag set, a target attribute tag that matches the content tag in the first tag set by using a method for calculating text similarity in the prior art or future development technology, such as euclidean distance, cosine distance, and jaccard similarity.
Specifically, the first tab set is { a1, a2}, the second tab set is { B1, B2, B3}, the executing entity may calculate the similarity between each tab in the second tab set and each tab in the first tab set, that is, the similarity S1 between a1 and B1, the similarity S2 between a1 and B2, the similarity S3 between a1 and B3, the similarity S4 between a2 and B1, the similarity S5 between a2 and B2, and the similarity S6 between a2 and B3, respectively, and may determine that B1 and B2 are the target attribute tabs if the preset text similarity threshold is K, where the similarities S1 and S5 are greater than or equal to the preset similarity threshold K.
Here, the preset text similarity threshold may be determined according to experience, actual requirements, and specific application scenarios.
And step 204, generating praise information of the recommended content based on the target attribute label and the praise user number corresponding to the target attribute label.
In this embodiment, after determining the target attribute tag, the execution subject may generate one or more praise information of the recommended content according to the target attribute tag and the praise user number corresponding to the target attribute tag.
Each piece of praise information may include a target attribute tag and the number of praise users corresponding to the tag.
Specifically, the number of the target attribute tags is two, for example, B1 and B2, the number of the praise users corresponding to each target attribute tag is 80 and 70, respectively, and the execution subject may generate two pieces of praise information of the recommended content according to the target attribute tags and the number of the praise users corresponding to the target attribute tags, for example, 80B 1 users praise the recommended content and 70B 2 users praise the recommended content.
The embodiment of the disclosure determines the target adjectives corresponding to the target attribute tags in a preset forward adjective library describing the user; and in response to the determination that the target attribute label is a multi-level label, generating praise information of the recommended content based on the name label of the last level in the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label, and improving the interestingness and readability of the praise information of the generated recommended content.
In some optional manners, generating praise information of the recommended content based on the target attribute tag and the number of praise users corresponding to the target attribute tag includes: and in response to the fact that the number of the target attribute tags is determined to be multiple, generating praise information of the recommended content based on the final target attribute tags and the corresponding praise user number.
In this implementation manner, after determining the target attribute tags, the execution subject may first determine the number of the target attribute tags, and if the number of the target attribute tags is multiple, further determine the final target attribute tags and the number of the corresponding praise users, and generate praise information of the recommended content based on the final target attribute tags and the number of the corresponding praise users, where the final target attribute tags are the target attribute tags with the largest number of the corresponding praise users among the multiple target attribute tags.
Specifically, the execution subject determines two target attribute tags, for example, B1 and B2, where the number of complimentary users corresponding to each target tag is 80 and 70, respectively, and further, the execution subject may determine a final target attribute tag, that is, a target attribute tag with the largest number of complimentary users in the plurality of target attribute tags, that is, a target attribute tag B1, and further generate complimentary information of the recommended content based on the final target attribute tag and the corresponding number of complimentary users, for example, the recommended content is complied with by 80B 1 users.
According to the implementation mode, in response to the fact that the number of the target attribute tags is determined to be multiple, the praise information of the recommended content is generated based on the final target attribute tags and the corresponding praise user number, the user can know the attribute tags with the largest corresponding praise user number in time, and the efficiency and the effectiveness of generating the praise information of the recommended content are effectively improved.
In some optional manners, generating praise information of the recommended content based on the target attribute tag and the number of praise users corresponding to the target attribute tag includes: and in response to the fact that the target attribute label is determined to be a multi-level label, generating praise information of the recommended content based on the name label of the last level in the target attribute label and the praise user number corresponding to the target attribute label.
In this implementation manner, after determining the target attribute tag, the execution subject may first determine whether the target tag is a multi-level tag, where each level of the multi-level tag includes a tag name in the application field, and if the target attribute tag is the multi-level tag, generate the praise information of the recommended content according to the last level of the target attribute tag and the number of praise users corresponding to the target attribute tag.
Specifically, the execution subject determines a target attribute tag, where the target attribute tag is a multi-level tag, for example, a first-level name tag L1, a second-level name tag L2, a third-level name tag L3, and a fourth-level name tag L4, where the number of complimentary users corresponding to the target attribute tag is 80, and the execution subject may generate complimentary information of the recommended content according to the last-level name tag in the target attribute tag, that is, the fourth-level name tag L4, and the number of complimentary users corresponding to the target attribute tag, for example, 80L 4 users approve the recommended content.
In the implementation mode, in response to the fact that the target attribute label is determined to be the multi-level label, the praise information of the recommended content is generated based on the name label of the last level in the target attribute label and the praise user number corresponding to the target attribute label, readability of the praise information is improved, and accurate understanding of the consumed content by the user is facilitated.
In some optional manners, generating praise information of the recommended content based on the target attribute tag and the number of praise users corresponding to the target attribute tag includes: determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user; and generating praise information of the recommended content based on the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
In the implementation mode, after the execution main body determines the target attribute label, one adjective can be randomly extracted from a pre-constructed forward adjective library for describing the user as a target adjective corresponding to the target attribute label; and generating praise information of the recommended content based on the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
Specifically, the execution main body determines a target attribute tag, for example, an interest tag K, where the number of complimentary users corresponding to the target attribute tag is 80, randomly extracts an adjective from a preset forward adjective library describing the user as a target adjective corresponding to the target attribute tag, for example, intelligently and intelligently, and further generates complimentary information of the recommended content based on the target attribute tag, the target adjective corresponding to the target attribute tag, and the number of complimentary users corresponding to the target attribute tag, where for example, the recommended content is complied by 80 smart K users.
The implementation mode determines a target adjective corresponding to a target attribute label in a preset forward adjective library describing a user; and generating the praise information of the recommended content based on the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label, so that the interestingness of the generated praise information of the recommended content is improved.
In some optional manners, generating praise information of the recommended content based on the target attribute tag and the number of praise users corresponding to the target attribute tag includes: determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user; and in response to the fact that the target attribute label is determined to be a multi-level label, generating praise information of the recommended content based on the name label of the last level in the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
In this implementation manner, if the target attribute tag is a multi-level tag, the execution main body may further generate praise information of the recommended content based on the name tag of the last level in the target attribute tag, the target adjective corresponding to the target attribute tag, and the praise user number corresponding to the target attribute tag.
Specifically, the execution subject determines a target attribute tag, which is a multi-level tag, for example, a first-level name tag L1, a second-level name tag L2, a third-level name tag L3, and a fourth-level name tag L4, where the number of complimentary users corresponding to the target attribute tag is 80, further, the execution subject may randomly extract an adjective from a preset forward adjective library describing the user based on an interesting conversational model as a target adjective corresponding to the target attribute tag, for example, smartly and intelligently, and generate complimentary information of the recommended content according to the last-level name tag in the target attribute tag, the target adjective corresponding to the target attribute tag, and the number of complimentary users corresponding to the target attribute tag, for example, the recommended content is complied by the 80-bit smartly and intelligently L4 user.
Here, the interesting word operation model is configured to determine a last-level name tag in the target attribute tags and a target adjective corresponding to the target attribute tags in response to determining that the target attribute tags are multi-level tags, and form a display word operation, that is, generate the like information of the recommended content according to the last-level name tag in the target attribute tags, the target adjective corresponding to the target attribute tags, and the number of like users corresponding to the target attribute tags.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the information generation method according to the present embodiment. The executing entity 301 may obtain a first tag set 302 consisting of content tags corresponding to recommended content in a wired or wireless manner, for example, { a, B, C }; acquiring a second tag set 303 consisting of attribute tags of praise users corresponding to recommended content, for example, { a1, B1} and the number 304, for example, 80, 60, of praise users corresponding to each tag in the set, wherein the attribute tags in the second tag set are different from each other; in the second tab set, the target property tab 305 is determined to match the content tab in the first tab set, e.g., a1, B1; and generating the praise information 306 of the recommended content based on the target attribute tag and the number of praise users corresponding to the target attribute tag, wherein for example, 80A 1 users praise the recommended content and 60B 1 users praise the recommended content.
With further reference to fig. 4, a flow 400 of yet another embodiment of the information generation method shown in fig. 2 is shown. In this embodiment, the flow 400 of the information generating method may include the following steps:
In this embodiment, details of implementation and technical effects of step 401 may refer to the description of step 201, and are not described herein again.
In this embodiment, reference may be made to the description of step 202 for details of implementation and technical effects of step 402, which are not described herein again.
In step 403, in the second tag set, the target attribute tag matched with the content tag in the first tag set is determined.
In this embodiment, reference may be made to the description of step 202 for details of implementation and technical effects of step 402, which are not described herein again.
In response to determining that the number of the target attribute tags is multiple, for each target attribute tag, determining a feature value of the target attribute tag based on a product of the weight of the target attribute tag and the number of the users corresponding to the target attribute tag.
In this embodiment, after determining the target attribute tags, the execution subject may first determine the number of the target attribute tags, and if the number of the target attribute tags is multiple, further determine a feature value corresponding to each target attribute tag, where the feature value may be determined according to a product of a weight of the target attribute tag and a number of approved users corresponding to the target attribute tag.
For each target attribute label, the weight of the target attribute label can be determined according to the accuracy of the content label representation content corresponding to the target attribute label.
And 405, sequencing the target attribute tags according to the size of the characteristic value corresponding to each target attribute tag to obtain a sequencing result.
In this embodiment, after obtaining the eigenvalue corresponding to each target attribute tag, the execution main body may sort the target attribute tags according to the attributes of which the eigenvalues are from large to small, so as to obtain a sorting result.
And 406, generating praise information of the recommended content based on the sequencing result.
In this embodiment, after obtaining the sorting result, the execution subject may generate the praise information of the recommended content according to the highest-ranked target attribute tag and the number of corresponding praise users, or may generate the praise information of the recommended content according to a plurality of target attribute tags ranked in the front and the number of corresponding praise users of each target attribute, which is not limited in this application.
Specifically, the execution subject determines two target attribute tags, for example, B1 and B2, the number of praise users corresponding to each target attribute tag is 80 and 70, respectively, the weight of each target attribute tag is 0.2 and 0.8, further, for each target attribute tag, based on the product of the weight of the target attribute tag and the number of praise users corresponding to the target attribute tag, the execution subject determines the feature value of the target attribute tag, that is, the feature value 16(80 x 0.2) and the feature value 56(70 x 0.8), and sorts the target attribute tags according to the size of the feature value corresponding to each target attribute tag to obtain a sorting result, and generates praise information of the recommended content based on the sorting result, for example, generates praise information of the recommended content based on the highest-ranked target attribute tag and the number of praise users corresponding to the target attribute tag, i.e., 70B 2 users approve of the recommendation.
In some optional manners, determining the feature value of the target attribute tag based on a product of the weight of the target attribute tag and the number of complimentary users corresponding to the target attribute tag includes: and determining an initial characteristic value of the target attribute label according to the product of the weight of the target attribute label and the number of praise users corresponding to the target attribute label, and determining the characteristic value of the target attribute label according to the ratio of the initial characteristic value to the total number of praise users.
In this implementation manner, after determining the target attribute tags, the execution subject may first determine the number of the target attribute tags, and if the number of the target attribute tags is multiple, for each target attribute tag, the execution subject may first determine an initial feature value of the target attribute tag according to a product of the weight of the target attribute tag and the number of complimentary users corresponding to the target attribute tag, and then determine a feature value of the target attribute tag according to a ratio of the initial feature value to the total number of complimentary users.
Specifically, the executive agent determines two target attribute tags, for example, B1 and B2, the number of users subscribed to the target tag is 80 and 70, the weight of each target tag is 0.2 and 0.8, and the total number of users subscribed to the target tag is 100, further, the executive agent determines an initial characteristic value of the target attribute tag, that is, an initial characteristic value 16(80 × 0.2) and an initial characteristic value 56(70 × 0.8), according to the product of the weight of the target attribute tag and the number of users subscribed to the target attribute tag, and further determines characteristic values of the target attribute tag, that is, a characteristic value 0.16(16/100) and a characteristic value 0.56(56/100), according to the ratio of the initial characteristic value to the total number of users subscribed to the target attribute tag, and sorts the target attribute tags according to the sizes of the characteristic values corresponding to the target attribute tags, and obtaining a sorting result, and generating praise information of the recommended content based on the sorting result, for example, generating praise information of the recommended content based on the highest-ranking target attribute tag and the number of corresponding praise users, that is, 70B 2 users praise the recommended content.
The implementation method determines an initial characteristic value of the target attribute tag according to the product of the weight of the target attribute tag and the number of praise users corresponding to the target attribute tag, determines the characteristic value of the target attribute tag according to the ratio of the initial characteristic value to the total number of praise users, then performs ranking based on the characteristic values to obtain a ranking result, and outputs praise information of recommended contents according to the ranking result, thereby being beneficial to improving the accuracy of crowd characteristics portrayed by the output praise information and further improving the effectiveness of the praise information.
Compared with the embodiment shown in fig. 2, the above-mentioned embodiment of the present disclosure highlights that in response to determining that the number of the target attribute tags is multiple, for each target attribute tag, the feature value of the target attribute tag is determined based on the product of the weight of the target attribute tag and the number of the praise users corresponding to the target attribute tag, the target attribute tags are sorted according to the size of the feature value corresponding to each target attribute tag to obtain a sorting result, and the praise information of the recommendation content is generated based on the sorting result, which is helpful for improving the accuracy of the crowd features depicted by the output praise information, and further improves the effectiveness of the praise information.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of an information generating apparatus, which corresponds to the method embodiment shown in fig. 1, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the information generating apparatus 500 of the present embodiment includes: a first acquisition module 501, a second acquisition module 502, a determination target module 503, and a generation information module 504.
The first obtaining module 501 may be configured to obtain a first tag set formed by content tags corresponding to recommended content.
The second obtaining module 502 may be configured to obtain a second tag set formed by attribute tags of praise users corresponding to the recommended content and the number of praise users corresponding to each tag in the set.
The determine target module 503 may be configured to determine target attribute tags in the second set of tags that match the content tags in the first set of tags.
The generate information module 504 may be configured to generate the like information of the recommended content based on the target attribute tag and the number of like users corresponding to the target attribute tag.
In some optional manners of this embodiment, determining the target module includes: the determining characteristic unit is configured to determine a characteristic value of each target attribute label based on the product of the weight of the target attribute label and the number of the users corresponding to the target attribute label in response to the fact that the number of the target attribute labels is multiple, wherein the weight of the target attribute label is determined according to the accuracy of the content represented by the content label corresponding to the target attribute label; the sequencing tag unit is configured to sequence the target attribute tags according to the size of the characteristic value corresponding to each target attribute tag to obtain a sequencing result; and an information generation unit configured to generate like information of the recommended content based on the sorting result.
In some alternatives of this embodiment, the determining the characteristic unit is further configured to: determining an initial characteristic value of the target attribute label according to the product of the weight of the target attribute label and the number of praise users corresponding to the target attribute label; and determining the characteristic value of the target attribute label according to the ratio of the initial characteristic value to the total number of the praise users.
In some alternatives of this embodiment, the information generating module is further configured to: and in response to the fact that the target attribute label is determined to be a multi-level label, generating praise information of the recommended content based on the name label of the last level in the target attribute label and the praise user number corresponding to the target attribute label.
In some alternatives of this embodiment, the information generating module is further configured to: determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user; and generating praise information of the recommended content based on the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
In some alternatives of this embodiment, the information generating module is further configured to: determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user; and in response to the fact that the target attribute label is determined to be a multi-level label, generating praise information of the recommended content based on the name label of the last level in the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
In some alternatives of this embodiment, the information generating module is further configured to: and in response to the fact that the number of the target attribute tags is determined to be multiple, generating praise information of the recommended content based on the final target attribute tags and the corresponding praise user number.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
As shown in fig. 6, is a block diagram of an electronic device of an information generating method according to an embodiment of the present disclosure.
600 is a block diagram of an electronic device in accordance with an information generation method of an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium provided by the present disclosure. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the information generating method provided by the present disclosure. The non-transitory computer-readable storage medium of the present disclosure stores computer instructions for causing a computer to execute the information generation method provided by the present disclosure.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the information generation method in the embodiments of the present disclosure (e.g., the first acquisition module 501, the second acquisition module 502, the target determination module 503, and the information generation module 504 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 602, that is, implements the information generation method in the above-described method embodiments.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by use of the electronic device for face tracking, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 may optionally include memory located remotely from the processor 601, which may be connected to lane line detection electronics over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the information generating method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the lane line detecting electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the disclosure, the crowd characteristic information behind the praise information can be displayed, and the information acquisition efficiency of the user is effectively improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (17)
1. An information generating method, comprising:
acquiring a first label set formed by content labels corresponding to recommended content;
acquiring a second label set formed by attribute labels of praise users corresponding to recommended contents and the number of the praise users corresponding to each label in the set, wherein the attribute labels in the second label set are different;
in a second label set, determining a target attribute label matched with a content label in the first label set;
and generating praise information of the recommended content based on the target attribute label and the praise user number corresponding to the target attribute label.
2. The method of claim 1, wherein generating the like information of the recommended content based on the target attribute tag and the number of like users corresponding to the target attribute tag comprises:
in response to determining that the number of the target attribute tags is multiple, for each target attribute tag, determining a characteristic value of the target attribute tag based on a product of a weight of the target attribute tag and a number of approved users corresponding to the target attribute tag, wherein the weight of the target attribute tag is determined according to accuracy of content tag characterization content corresponding to the target attribute tag;
sequencing the target attribute tags according to the size of the characteristic value corresponding to each target attribute tag to obtain a sequencing result;
and generating praise information of the recommended content based on the sequencing result.
3. The method of claim 2, wherein the determining the feature value of the target attribute tag based on the product of the weight of the target attribute tag and the number of complimentary users corresponding to the target attribute tag comprises:
determining an initial characteristic value of the target attribute label according to the product of the weight of the target attribute label and the number of praise users corresponding to the target attribute label;
and determining the characteristic value of the target attribute label according to the ratio of the initial characteristic value to the total number of the praise users.
4. The method of claim 1, wherein the generating of the like information of the recommended content based on the target attribute tag and the number of like users corresponding to the target attribute tag comprises:
and in response to determining that the target attribute tag is a multi-level tag, generating praise information of the recommended content based on the name tag of the last level in the target attribute tag and the number of praise users corresponding to the target attribute tag, wherein each level in the multi-level tag comprises a tag name of the application field.
5. The method of claim 1, wherein generating the like information of the recommended content based on the target attribute tag and the number of like users corresponding to the target attribute tag comprises:
determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user;
and generating praise information of the recommended content based on the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
6. The method of claim 1, wherein generating the like information of the recommended content based on the target attribute tag and the number of like users corresponding to the target attribute tag comprises:
determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user;
and in response to determining that the target attribute tag is a multi-level tag, generating praise information of the recommended content based on the name tag of the last level in the target attribute tag, the target adjective corresponding to the target attribute tag and the praise user number corresponding to the target attribute tag, wherein each level in the multi-level tag comprises a tag name of the application field.
7. The method of claim 1, wherein generating the like information of the recommended content based on the target attribute tag and the number of like users corresponding to the target attribute tag comprises:
and in response to determining that the number of the target attribute tags is multiple, generating praise information of the recommended content based on the final target attribute tag and the corresponding praise user number, wherein the final target attribute tag is the target attribute tag with the maximum corresponding praise user number in the multiple target attribute tags.
8. An information generating apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire a first label set formed by content labels corresponding to recommended content;
the second acquisition module is configured to acquire a second tag set formed by attribute tags of praise users corresponding to the recommended content and the number of praise users corresponding to each tag in the set, wherein the attribute tags in the second tag set are different from each other;
a target determination module configured to determine a target attribute tag in a second tag set, the target attribute tag matching a content tag in the first tag set;
and the information generation module is configured to generate praise information of the recommended content based on the target attribute tag and the praise user number corresponding to the target attribute tag.
9. The apparatus of claim 8, wherein the determine target module comprises:
the determining characteristic unit is configured to determine a characteristic value of each target attribute label based on the product of the weight of the target attribute label and the number of the users corresponding to the target attribute label in response to the fact that the number of the target attribute labels is multiple, wherein the weight of the target attribute label is determined according to the accuracy of the content represented by the content label corresponding to the target attribute label;
the sequencing tag unit is configured to sequence the target attribute tags according to the size of the characteristic value corresponding to each target attribute tag to obtain a sequencing result;
and the generation information unit is configured to generate praise information of the recommended content based on the sorting result.
10. The apparatus of claim 9, wherein the determine features unit is further configured to:
determining an initial characteristic value of the target attribute label according to the product of the weight of the target attribute label and the number of praise users corresponding to the target attribute label;
and determining the characteristic value of the target attribute label according to the ratio of the initial characteristic value to the total number of the praise users.
11. The apparatus of claim 8, wherein the generate information module is further configured to:
and in response to determining that the target attribute tag is a multi-level tag, generating praise information of the recommended content based on the name tag of the last level in the target attribute tag and the number of praise users corresponding to the target attribute tag, wherein each level in the multi-level tag comprises a tag name of the application field.
12. The apparatus of claim 8, wherein the generate information module is further configured to:
determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user;
and generating praise information of the recommended content based on the target attribute label, the target adjective corresponding to the target attribute label and the praise user number corresponding to the target attribute label.
13. The apparatus of claim 8, wherein the generate information module is further configured to:
determining a target adjective corresponding to the target attribute label in a preset forward adjective library describing a user;
and in response to determining that the target attribute tag is a multi-level tag, generating praise information of the recommended content based on the name tag of the last level in the target attribute tag, the target adjective corresponding to the target attribute tag and the praise user number corresponding to the target attribute tag, wherein each level in the multi-level tag comprises a tag name of the application field.
14. The apparatus of claim 8, wherein the generate information module is further configured to:
and in response to determining that the number of the target attribute tags is multiple, generating praise information of the recommended content based on the final target attribute tag and the corresponding praise user number, wherein the final target attribute tag is the target attribute tag with the maximum corresponding praise user number in the multiple target attribute tags.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory is stored with instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.
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