CN116188120B - Method, device and system for recommending audio books and storage medium - Google Patents

Method, device and system for recommending audio books and storage medium Download PDF

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CN116188120B
CN116188120B CN202310472902.4A CN202310472902A CN116188120B CN 116188120 B CN116188120 B CN 116188120B CN 202310472902 A CN202310472902 A CN 202310472902A CN 116188120 B CN116188120 B CN 116188120B
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CN116188120A (en
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尹鹏飞
冯宇
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Beijing Huayue Jiacheng Technology Development Co ltd
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Abstract

The invention discloses a method, a device, a system and a storage medium for recommending a voice book, which comprise the steps of obtaining category information of the voice book in a voice book database; respectively extracting a plurality of corresponding characteristic keywords from the audio books according to the category information, counting, and reclassifying the audio books of the same category according to the quantity distribution information of the characteristic keywords; generating a content feature vector corresponding to the voice book; according to the content feature vector, a reclassified voice book category feature vector information base is established; acquiring a user characteristic vector of a target user according to user historical operation information; according to the voice book category feature vector information library matched with the user feature vector, after the matching degree of the voice book category feature vector matched with the user feature vector reaches a preset value, the user feature vector is regarded as the content of interest of the reader; the characteristics of the audio book can form different style types according to the keyword quantity distribution information, so that the style type bias is included in the characteristics; the pushing content is more accurate.

Description

Method, device and system for recommending audio books and storage medium
Technical Field
The invention belongs to the technical field of audio book recommendation, and particularly relates to an audio book recommendation method, device and system and a storage medium.
Background
In the internet age, traditional voice writing has presented significant challenges. First, the mass of time occupied by the large amount of fragmented content on the internet results in less and less time for listening to books, especially with great impact on knowledge-oriented excellent books. Second, as the variety of audio books published increases, more and more audio books of the same theme are available for people to choose from. Because traditional audio book recommendation mainly uses audio book information and propaganda notices of audio book issuers, people can hardly select through the short information, and good recommendation effect can not be achieved. Third, the form of the audio bibliographic information is single and insufficient to arouse people's reading interests. Moreover, the publicity notice issued by the audio book is only aimed at all people, and the audio book is not targeted, is difficult to meet the personalized demands of people and is not easy to arouse the interests of people. These problems all result in a difficulty in effectively utilizing the resources of the audio book library.
Disclosure of Invention
The invention aims to provide a method, a device, a system and a storage medium for recommending a voice book, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a method for recommending a voice book, which comprises the following steps:
acquiring category information of the audio book in the audio book database;
respectively extracting a plurality of corresponding characteristic keywords from different categories of audiobooks according to the category information, and counting the characteristic keywords so as to acquire the quantity distribution information of the characteristic keywords;
reclassifying the voice books of the same category according to the quantity distribution information of the characteristic keywords;
generating content feature vectors corresponding to the audio books according to the quantity distribution information of the feature keywords corresponding to the audio books;
according to the content feature vector, a reclassified voice book category feature vector information base is established;
acquiring historical operation information of a target user, and acquiring a user feature vector of the target user according to the historical operation information;
matching the voice book category feature vector information base according to the user feature vector, and considering the content of interest of the reader after the matching degree of the user feature vector matching the voice book category feature vector reaches a preset value; so as to acquire the voice book information preferred by the user, and pushing the corresponding voice book to the user according to the voice book information preferred by the user.
According to the technology, the corresponding characteristic keywords are respectively extracted from the voice books of different categories according to the category information, and the voice books of the same category are reclassified according to the quantity distribution information of the characteristic keywords; the classification is finer, and accurate pushing can be performed; in addition, counting a plurality of feature keywords so as to acquire the quantity distribution information of the feature keywords; generating content feature vectors corresponding to the audio books according to the quantity distribution information of the feature keywords corresponding to the audio books; the characteristics of the audio book can distribute different style types of information according to the number of keywords, so that the bias of the style types is included in the characteristics; the pushing content is more accurate.
In one possible design, the method for respectively extracting the corresponding feature keywords for the voice books of different categories according to the category information includes:
establishing a first-level keyword library of a specified category in category information;
carrying out semantic analysis on keywords in the first-level keyword library to obtain a second-level associated keyword library;
and respectively extracting a plurality of corresponding characteristic keywords for the voice books of the corresponding categories according to the keywords in the first-level keyword library and the second-level associated keyword library.
According to the technology, the corresponding characteristic keywords are respectively extracted from the voice books of the corresponding categories according to the keywords in the first-level keyword library and the second-level associated keyword library, and the two-level keywords can enable the recommended content to be accurately recommended in association, so that the content richness when the content is recommended is improved, and meanwhile, the pushing accuracy is improved.
In one possible design, the method further comprises:
extracting keywords in historical browsing and historical reading information of each user, and acquiring initial weights of users corresponding to the keywords;
according to the historical browsing and historical reading information between the target user and each other user, calculating the association weight of the other users to the target user;
calculating the similarity between the target user and other users according to the initial weight and the association weight, selecting a plurality of similar users similar to the target user from the other users according to the similarity, acquiring the weight of the similar users on each audio book content, and calculating the association coefficient of the target user on each audio book content according to the weight; and sequencing the voice book contents according to the association coefficient, and pushing the corresponding voice book contents to the target user according to the sequencing result.
Through the technology, the associated recommendation of similar users can be performed through the similarity among the users, so that the diversity and the expansibility of the content are improved.
In one possible design, calculating user feature vectors according to historical browsing and historical reading information of all users, matching the feature vectors of all voice book contents with the user feature vectors of all users, and performing hotness prediction on voice book contents to be pushed; and continuously correcting the association coefficient according to the user hit rate of the user real-time access data and the push data, sorting each audio book content according to the corrected association coefficient, and pushing the corresponding audio book content to the target user according to the sorting result.
In one possible design, the audio book category feature vector further includes: the method comprises the steps of classifying the audio book content, proportion of page browsing amount of the audio book content to average page browsing amount of each audio book content, page browsing amount of the audio book content in different time periods, change rate of page browsing amount of the audio book content in different time periods, generation time of the audio book content, time information corresponding to the page browsing amount of the audio book content and display position of the audio book content in a webpage.
In one possible design, the obtaining the historical operating information of the target user includes a record that the target user forwards the posting information of the other user, a record that the target user reviews the posting information of the other user, a record that the target user references the posting information of the other user, and a record that the target user links with the other user.
In one possible design, the step of generating the content feature vector corresponding to the vocal book according to the number distribution information of the feature keywords corresponding to the vocal book includes: extracting the quantity distribution information of characteristic keywords of the voice book content from a database; modeling the audio book content according to the quantity distribution information of the characteristic keywords to obtain a model of the audio book content; and constructing a content feature vector according to the model.
The second aspect of the invention provides a recommendation device for a sound book, which comprises a memory and a processor, wherein the memory and the processor are connected with each other through a bus; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory, causing the processor to perform the method of recommending a vocal book as described in the first aspect and any one of the possible designs of the first aspect.
A third aspect of the present invention provides a recommendation system for a voice book comprising a recommendation device as described in the second aspect, and a number of user terminals communicating with the recommendation device via the internet.
A fourth aspect of the present invention provides a storage medium having instructions stored thereon which, when executed on a computer, perform a method of recommending a vocal book as described in the first aspect and any one of the possible designs of the first aspect.
The beneficial effects are that: and the association model is built through the audio book data, the total log data and the big data, so that various data are effectively utilized, and the accuracy of the association model prediction can be improved. When the voice book pushing is needed to be carried out on the user, the user is facilitated to analyze the interest of the user by acquiring the log data and the book listening data of the user, and then the voice book matched and associated based on the association model is pushed to the user, so that the pertinence is high, the likelihood that the pushed voice book meets the user requirement is high, the borrowing probability of the user can be improved, and the utilization rate of the voice book resources is improved. The method comprises the steps of respectively extracting a plurality of corresponding characteristic keywords from different categories of audiobooks according to category information, and reclassifying the audiobooks of the same category according to quantity distribution information of the characteristic keywords; the classification is finer, and accurate pushing can be performed; in addition, counting a plurality of feature keywords so as to acquire the quantity distribution information of the feature keywords; generating content feature vectors corresponding to the audio books according to the quantity distribution information of the feature keywords corresponding to the audio books; the characteristics of the audio book can distribute different style types of information according to the number of keywords, so that the bias of the style types is included in the characteristics; the pushing content is more accurate; and carrying out associated recommendation of similar users through the similarity among the users, so that the diversity and the expansibility of the content are increased.
Drawings
Fig. 1 is a schematic flow chart of a method for recommending a voice book according to a first aspect of the embodiment.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
Examples:
as shown in fig. 1, the present embodiment provides a method for recommending a voice book according to a first aspect of the present invention, including the following steps:
acquiring category information of the audio book in the audio book database;
respectively extracting a plurality of corresponding characteristic keywords from different categories of audiobooks according to the category information, and counting the characteristic keywords so as to acquire the quantity distribution information of the characteristic keywords;
reclassifying the voice books of the same category according to the quantity distribution information of the characteristic keywords;
generating content feature vectors corresponding to the audio books according to the quantity distribution information of the feature keywords corresponding to the audio books;
according to the content feature vector, a reclassified voice book category feature vector information base is established;
acquiring historical operation information of a target user, and acquiring a user feature vector of the target user according to the historical operation information;
matching the voice book category feature vector information base according to the user feature vector, and considering the content of interest of the reader after the matching degree of the user feature vector matching the voice book category feature vector reaches a preset value; so as to acquire the voice book information preferred by the user, and pushing the corresponding voice book to the user according to the voice book information preferred by the user.
In one possible implementation manner, the method for respectively extracting the corresponding plurality of feature keywords for the voice books of different categories according to the category information includes:
establishing a first-level keyword library of a specified category in category information;
carrying out semantic analysis on keywords in the first-level keyword library to obtain a second-level associated keyword library;
and respectively extracting a plurality of corresponding characteristic keywords for the voice books of the corresponding categories according to the keywords in the first-level keyword library and the second-level associated keyword library.
In one possible embodiment, the method further comprises:
extracting keywords in historical browsing and historical reading information of each user, and acquiring initial weights of users corresponding to the keywords;
according to the historical browsing and historical reading information between the target user and each other user, calculating the association weight of the other users to the target user;
calculating the similarity between the target user and other users according to the initial weight and the association weight, selecting a plurality of similar users similar to the target user from the other users according to the similarity, acquiring the weight of the similar users on each audio book content, and calculating the association coefficient of the target user on each audio book content according to the weight; and sequencing the voice book contents according to the association coefficient, and pushing the corresponding voice book contents to the target user according to the sequencing result.
In one possible implementation, calculating user feature vectors according to historical browsing and historical reading information of all users, matching the feature vectors of all voice book contents with the user feature vectors of all users, and performing hotness prediction on voice book contents to be pushed; and continuously correcting the association coefficient according to the user hit rate of the user real-time access data and the push data, sorting each audio book content according to the corrected association coefficient, and pushing the corresponding audio book content to the target user according to the sorting result.
In one possible implementation manner, the audio book category feature vector further includes a category of audio book content, a ratio of a page browsing amount of the audio book content to an average page browsing amount of each audio book content, a page browsing amount in different periods of the audio book content, a rate of change of the page browsing amount in different periods of the audio book content, a time of generation of the audio book content, time information corresponding to the page browsing amount of the audio book content, and a display position of the audio book content in the web page.
In one possible implementation, the obtaining the historical operation information of the target user includes a record that the target user forwards the posting information of the other user, a record that the target user reviews the posting information of the other user, a record that the target user references the posting information of the other user, and a record that the target user links with the other user.
In one possible implementation manner, the step of generating the content feature vector corresponding to the audio book according to the number distribution information of the feature keywords corresponding to the audio book includes: extracting the quantity distribution information of characteristic keywords of the voice book content from a database; modeling the audio book content according to the quantity distribution information of the characteristic keywords to obtain a model of the audio book content; and constructing a content feature vector according to the model.
And the association model is built through the audio book data, the total log data and the big data, so that various data are effectively utilized, and the accuracy of the association model prediction can be improved. When the voice book pushing is needed to be carried out on the user, the user is facilitated to analyze the interest of the user by acquiring the log data and the book listening data of the user, and then the voice book matched and associated based on the association model is pushed to the user, so that the pertinence is high, the likelihood that the pushed voice book meets the user requirement is high, the borrowing probability of the user can be improved, and the utilization rate of the voice book resources is improved. The method comprises the steps of respectively extracting a plurality of corresponding characteristic keywords from different categories of audiobooks according to category information, and reclassifying the audiobooks of the same category according to quantity distribution information of the characteristic keywords; the classification is finer, and accurate pushing can be performed; in addition, counting a plurality of feature keywords so as to acquire the quantity distribution information of the feature keywords; generating content feature vectors corresponding to the audio books according to the quantity distribution information of the feature keywords corresponding to the audio books; the characteristics of the audio book can form different style types according to the keyword quantity distribution information, so that the style type bias is included in the characteristics; the pushing content is more accurate; and carrying out associated recommendation of similar users through the similarity among the users, so that the diversity and the expansibility of the content are increased.
The second aspect of the invention provides a recommendation device for a sound book, which comprises a memory and a processor, wherein the memory and the processor are connected with each other through a bus; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory, causing the processor to perform the method of recommending a vocal book as described in the first aspect and any one of the possible designs of the first aspect. By way of specific example, the Memory may include, but is not limited to, random-Access Memory (RAM), read-Only Memory (ROM), flash Memory (Flash Memory), first-in first-out Memory (First Input First Output, FIFO), and/or first-in last-out Memory (First Input Last Output, FILO), etc.; the processor may not be limited to use with a processor of the type X86, internet series or other microprocessor; the transceiver may be, but is not limited to, a wired transceiver, a wireless fidelity (Wireless Fidelity, wiFi) wireless transceiver, a bluetooth wireless transceiver, a general packet radio service technology (General Packet Radio Service, GPRS) wireless transceiver, and/or a ZigBee wireless transceiver (low power local area network protocol based on the ieee802.15.4 standard), etc. The electronic device may also include, but is not limited to, a power module, a display screen, and other necessary components.
A third aspect of the present invention provides a recommendation system for a voice book comprising a recommendation device as described in the second aspect, and a number of user terminals communicating with the recommendation device via the internet.
A fourth aspect of the present invention provides a storage medium having instructions stored thereon which, when executed on a computer, perform a method of recommending a vocal book as described in the first aspect and any one of the possible designs of the first aspect. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), etc., where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
The working process, working details and technical effects of the foregoing computer readable storage medium provided in the fourth aspect of the present embodiment may be referred to the interaction method described in the foregoing first aspect or any one of possible implementation manners of the first aspect, which are not described herein again.
A fifth aspect of the present embodiment provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of recommending a vocal book as described in the first aspect or any of the possible implementations of the first aspect. Wherein the computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices.
In summary, in the above embodiments, the correlation model is built by using the audio book data, the total log data and such big data, so that various data are effectively utilized, and the accuracy of the correlation model prediction can be improved. When the voice book pushing is needed to be carried out on the user, the user is facilitated to analyze the interest of the user by acquiring the log data and the book listening data of the user, and then the voice book matched and associated based on the association model is pushed to the user, so that the pertinence is high, the likelihood that the pushed voice book meets the user requirement is high, the borrowing probability of the user can be improved, and the utilization rate of the voice book resources is improved. The method comprises the steps of respectively extracting a plurality of corresponding characteristic keywords from different categories of audiobooks according to category information, and reclassifying the audiobooks of the same category according to quantity distribution information of the characteristic keywords; the classification is finer, and accurate pushing can be performed; in addition, counting a plurality of feature keywords so as to acquire the quantity distribution information of the feature keywords; generating content feature vectors corresponding to the audio books according to the quantity distribution information of the feature keywords corresponding to the audio books; the characteristics of the audio book can distribute different style types of information according to the number of keywords, so that the bias of the style types is included in the characteristics; the pushing content is more accurate; and carrying out associated recommendation of similar users through the similarity among the users, so that the diversity and the expansibility of the content are increased.
The embodiments described above are merely illustrative and may or may not be physically separate if reference is made to the unit being described as a separate component; if a component is referred to as being a unit, it may or may not be a physical unit, may be located in one place, or may be distributed over multiple network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for recommending a voice book, comprising the steps of:
acquiring category information of the audio book from an audio book database;
respectively extracting a plurality of corresponding characteristic keywords from different categories of audiobooks according to the category information, and counting the characteristic keywords so as to acquire the quantity distribution information of the characteristic keywords;
reclassifying the corresponding category of the audio books according to the quantity distribution information of the characteristic keywords;
generating content feature vectors corresponding to the reclassified voice books according to the quantity distribution information of the feature keywords corresponding to the reclassified voice books;
according to the content feature vector, a reclassified voice book category feature vector information base is established;
acquiring historical operation information of a target user, and acquiring a user feature vector of the target user according to the historical operation information;
according to the matching of the user feature vector and the voice book category feature vector information base, judging whether the matching degree of the user feature vector and the voice book category feature vector reaches a preset value or not;
if yes, the voice book corresponding to the voice book category feature vector is used as voice book information preferred by the user;
pushing corresponding audio books to the user according to the audio book information preferred by the user;
the method for respectively extracting the corresponding multiple characteristic keywords from the voice books of different categories according to the category information comprises the following steps:
establishing a first-level keyword library of a specified category in category information;
carrying out semantic analysis on keywords in the first-level keyword library to obtain a second-level associated keyword library;
and respectively extracting a plurality of corresponding characteristic keywords for the voice books of the corresponding categories according to the keywords in the first-level keyword library and the second-level associated keyword library.
2. The method of claim 1, further comprising:
extracting keywords in historical browsing and historical reading information of each user, and acquiring initial weights of users corresponding to the keywords;
according to the historical browsing and historical reading information between the target user and each other user, calculating the association weight of the other users to the target user;
calculating the similarity between the target user and other users according to the initial weight and the association weight, selecting a plurality of similar users similar to the target user from the other users according to the similarity, acquiring the weight of the similar users on each audio book content, and calculating the association coefficient of the target user on each audio book content according to the weight; and sequencing the voice book contents according to the association coefficient, and pushing the corresponding voice book contents to the target user according to the sequencing result.
3. The method for recommending a voice book according to claim 2, wherein the user feature vectors are calculated according to the historical browsing and historical reading information of all users, the feature vectors of all voice book contents are matched with the user feature vectors of all users, and the voice book contents to be pushed are subjected to hotness prediction; and continuously correcting the association coefficient according to the user hit rate of the user real-time access data and the push data, sorting each audio book content according to the corrected association coefficient, and pushing the corresponding audio book content to the target user according to the sorting result.
4. The method of claim 1, wherein the audio book category feature vector further comprises: the method comprises the steps of classifying the audio book content, proportion of page browsing amount of the audio book content to average page browsing amount of each audio book content, page browsing amount of the audio book content in different time periods, change rate of page browsing amount of the audio book content in different time periods, generation time of the audio book content, time information corresponding to the page browsing amount of the audio book content and display position of the audio book content in a webpage.
5. The method of claim 1, wherein the step of generating the content feature vector corresponding to the audio book according to the number distribution information of the feature keywords corresponding to the audio book comprises: extracting the quantity distribution information of characteristic keywords of the voice book content from a database; modeling the audio book content according to the quantity distribution information of the characteristic keywords to obtain a model of the audio book content; and constructing a content feature vector according to the model.
6. The recommendation device for the audio book is characterized by comprising a memory and a processor, wherein the memory and the processor are connected through a bus; the memory stores computer-executable instructions; the processor executing computer-executable instructions stored in a memory, causing the processor to perform the method of recommending a voice book according to any one of claims 1 to 5.
7. A voice book recommendation system comprising a recommendation device as claimed in claim 6, and a number of user terminals communicating with the recommendation device via the internet.
8. A storage medium having instructions stored thereon which, when executed on a computer, perform the method of recommending a voice book according to any one of claims 1 to 5.
CN202310472902.4A 2023-04-28 2023-04-28 Method, device and system for recommending audio books and storage medium Active CN116188120B (en)

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