CN108984688B - Mother and infant knowledge topic recommendation method and device - Google Patents
Mother and infant knowledge topic recommendation method and device Download PDFInfo
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- CN108984688B CN108984688B CN201810719230.1A CN201810719230A CN108984688B CN 108984688 B CN108984688 B CN 108984688B CN 201810719230 A CN201810719230 A CN 201810719230A CN 108984688 B CN108984688 B CN 108984688B
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Abstract
The embodiment of the application provides a mother and infant knowledge topic recommendation method and device. The method comprises the following steps: aiming at each mother and infant commodity, acquiring commodity description information of the mother and infant commodity and a mother and infant knowledge question bank related to the mother and infant commodity; calculating the topic similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a topic similarity set; and selecting the mother-infant knowledge topic documents meeting preset rules according to the theme similarity set to serve as recommendation documents of the mother-infant commodities. Therefore, the mother and infant knowledge topics related to the mother and infant commodities recommended and browsed by the mother and infant user can be provided for the mother and infant user when the mother and infant commodity is browsed, so that the mother and infant user can be helped to know the commodities and the mother and infant knowledge more, the mother and infant user can actively participate in discussion, and commodity contents are generated.
Description
Technical Field
The application relates to the field of text semantic analysis, in particular to a mother and infant knowledge topic recommendation method and device.
Background
The detail page of the maternal and infant commodities is the most important part of the maternal and infant electronic commerce website and is generally mainly composed of commodity descriptions and commodity pictures. The general commodity description is filled by the operation of a merchant or a website, and is more formal and standard. The selection of the mother and infant commodities is special, besides the basic description of the characteristics, price and the like of the commodities, most of the time, the breeding knowledge corresponding to the commodities needs to be known, and further, the discussion about the breeding topics of the commodities, which is needed by mother and infant user groups, needs to be met, and the like. How to recommend incubation knowledge related to commodities and incubation topics related to the commodities for mother and infant users so as to help the mother and infant users to know the commodities and the mother and infant knowledge more is a technical problem to be solved urgently by technical staff in the field.
Disclosure of Invention
In order to overcome the defects in the prior art, the application aims to provide a maternal and infant knowledge topic recommendation method and device, which can recommend and browse maternal and infant knowledge topics related to maternal and infant commodities for a maternal and infant user when the maternal and infant user browses the maternal and infant commodities, so that the maternal and infant user can know the commodities and the maternal and infant knowledge more, the maternal and infant user actively participates in discussion, and commodity contents are generated.
In order to achieve the above purpose, the embodiments of the present application employ the following technical solutions:
in a first aspect, an embodiment of the present application provides a mother-infant knowledge topic recommendation method, which is applied to a server, and the method includes:
aiming at each mother and infant commodity, acquiring commodity description information of the mother and infant commodity and a mother and infant knowledge question bank related to the mother and infant commodity;
calculating the topic similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a topic similarity set;
and selecting the mother-infant knowledge topic documents meeting preset rules according to the theme similarity set to serve as recommendation documents of the mother-infant commodities.
Optionally, the step of obtaining the commodity description information of the mother and infant commodity and the mother and infant knowledge topic library associated with the mother and infant commodity includes:
acquiring commodity feature information of the mother and infant commodity in a current display page, and taking the commodity feature information as commodity description information, wherein the commodity feature information at least comprises a commodity name, a commodity classification and a commodity title;
and retrieving a mother-infant knowledge question bank related to the mother-infant commodity based on the commodity characteristic information.
Optionally, the step of calculating the topic similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a topic similarity set includes:
segmenting each mother-infant knowledge topic document in the mother-infant knowledge topic library to obtain a plurality of words in each mother-infant knowledge topic document;
inputting a plurality of words after word segmentation into an LDA model as LDA model input vectors to train so as to obtain a theme model expression of the mother-infant knowledge topic library, wherein the theme model expression comprises a theme representation of each mother-infant knowledge topic document in the mother-infant knowledge topic library, and the theme representation comprises theme importance degrees of each theme in the mother-infant knowledge topic document;
calculating by combining a TWE model and the topic model expression to obtain an embedded expression of each topic and an embedded expression of each word;
calculating the probability of generating the commodity description information by each mother-infant knowledge topic document in the mother-infant knowledge topic library based on the topic model expression, the embedded expression of each topic and the embedded expression of each word, and taking the probability as the topic similarity between each mother-infant knowledge topic document and the commodity description information.
Optionally, the calculation formula for calculating the probability of generating the commodity description information for each maternal and infant knowledge topic document in the maternal and infant knowledge topic library based on the topic model expression, the embedded expression of each topic, and the embedded expression of each word is as follows:
wherein Sim (s, d) is subject similarity between the commodity description information s and the mother-infant knowledge topic document d, and P (w | z |)k) As an embedded expression for each word w and each topic zkTopic similarity between embedded expressions of P (z)kD) for each topic zkAnd topic similarity between the embedded expression of (a) and the topic representation of each maternal-fetal knowledge topic document d.
Optionally, the step of selecting the mother-infant knowledge topic document meeting a preset rule according to the topic similarity set as the recommendation document of the mother-infant commodity includes:
filtering mother and infant knowledge topic documents corresponding to the topic similarity smaller than a first preset threshold value in the topic similarity set;
sequencing the rest mother and infant knowledge topic documents after filtering according to the topic similarity, and generating a sequencing result;
and selecting the mother and infant knowledge topic documents meeting the preset rules according to the sequencing result to serve as the recommendation documents of the mother and infant commodities.
Optionally, the step of selecting the mother-infant knowledge topic document meeting the preset rule according to the sorting result as the recommendation document of the mother-infant commodity includes:
selecting the mother and infant knowledge topic documents with the topic similarity in the top N number in the sequencing result as recommendation documents of the mother and infant commodities; or
And selecting all mother and infant knowledge topic documents with the theme similarity larger than a second preset threshold value in the sequencing result as recommendation documents of the mother and infant commodities.
Optionally, after the step of selecting the mother-infant knowledge topic document meeting the preset rule according to the topic similarity set as the recommendation document of the mother-infant commodity, the method includes:
and when the opening of the commodity display page of the maternal and infant commodity is detected, displaying the recommendation document of the maternal and infant commodity in the commodity display page of the maternal and infant commodity.
In a second aspect, the embodiment of the application further provides a mother and infant knowledge topic recommendation device, which is applied to a server, and the device includes:
the acquisition module is used for acquiring commodity description information of each mother and infant commodity and a mother and infant knowledge question bank related to the mother and infant commodity;
the calculation module is used for calculating the theme similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a theme similarity set;
and the selecting module is used for selecting the maternal and infant knowledge topic documents meeting the preset rules as the recommendation documents of the maternal and infant commodities according to the theme similarity set.
In a third aspect, an embodiment of the present application further provides a readable storage medium, where a computer program is stored in the readable storage medium, and the computer program, when executed, implements the maternal and infant knowledge topic recommendation method described above.
Compared with the prior art, the method has the following beneficial effects:
according to the method and the device for recommending the maternal and infant knowledge topics, firstly, commodity description information of each maternal and infant commodity and a maternal and infant knowledge topic library related to the maternal and infant commodity are obtained for each maternal and infant commodity. And then, calculating the theme similarity between the commodity description information and each mother-infant knowledge topic document in the mother-infant knowledge topic library to obtain a theme similarity set. And finally, selecting the mother-infant knowledge topic documents meeting the preset rules according to the theme similarity set to serve as recommendation documents of the mother-infant commodities. By adopting the technical scheme, the mother and infant knowledge topics related to the mother and infant commodities recommended and browsed for the mother and infant users can be provided when the mother and infant users browse the mother and infant commodities, so that the mother and infant users can know the commodities and the mother and infant knowledge more, the mother and infant users can actively participate in discussion, and commodity contents are generated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and it will be apparent to those skilled in the art that other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic view of an application scenario of a mother-infant knowledge topic recommendation method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a mother-infant knowledge topic recommendation method according to an embodiment of the present application;
fig. 3 is another schematic flow chart of a mother-infant knowledge topic recommendation method provided in an embodiment of the present application;
fig. 4 is a block diagram of a server for implementing the mother-infant knowledge topic recommendation method according to an embodiment of the present application;
fig. 5 is a functional block diagram of the mother-infant knowledge topic recommendation device shown in fig. 4.
Icon: 100-a server; 110-a bus; 120-a processor; 130-a storage medium; 140-bus interface; 150-a network adapter; 160-a user interface; 200-mother and infant knowledge topic recommendation device; 210-an obtaining module; 220-a calculation module; 230-a selection module; 300-user terminal.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Please refer to fig. 1, which is a schematic view of an application scenario of the mother-infant knowledge topic recommendation method according to the embodiment of the present application. In this embodiment, the application scenario includes at least one user terminal 300 and a server 100 in communication connection with the at least one user terminal 300.
In this embodiment, the user terminal 300 may be a personal computer, a smart phone, a tablet computer, a wearable device, and the like, which is not limited in detail in this embodiment.
According to some embodiments of the present application, the user terminal 300 may include: a processing device including an application processing section and a radio frequency/digital signal processor; a display screen; a keypad that may include physical keys, touch keys overlaid on a display, or a combination thereof; a subscriber identity module card; memory devices that may include ROM, RAM, flash memory, or any combination thereof; a Wi-Fi and/or Bluetooth interface; the NFC chip, the wireless power receiving coil used for wireless charging and the wireless telephone interface; a power management circuit with an associated battery; a USB interface and a connector; an audio management system with associated microphone, speaker and headphone jack; and various optional accessory components such as cameras, global positioning systems, accelerators, etc. In addition, various client applications may be installed on the user terminal 300, which may be used to allow the user terminal 300 to be used to communicate commands suitable for operation with other devices. Such applications may be downloaded from the server 100 and installed in the memory of the user terminal 300, or may have been previously installed on the user terminal 300. In the embodiment of the present application, the user terminal 300 may be installed with a mother and infant commodity application (which may be an APP application, a wechat applet, a pay-for-all applet, a WEB light application, and the like). The maternal and infant commodity application can point a user to realize functions of user registration, user login, commodity collection, maternal and infant commodity browsing, maternal and infant commodity purchasing, maternal and infant knowledge topic browsing, user invitation and the like.
In this embodiment, the server 100 should be understood as a service point providing processes, databases, and communication facilities. By way of example, server 100 may refer to a single physical processor with associated communication and data storage and library facilities, or it may refer to a networked or clustered collection of processors, associated networks, and storage devices, and operating on software and one or more library systems and application software that support the services provided by server 100. The servers 100 may vary widely in configuration or performance, but the servers 100 may generally include one or more central processing units and memory units. The Server 100 may also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network components, one or more input/output components, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and so forth.
Further, please refer to fig. 2, which is a schematic flowchart of a mother-infant knowledge topic recommendation method provided in an embodiment of the present application, and the method is executed by the server 100 shown in fig. 1. It should be noted that the mother-infant knowledge topic recommendation method provided in the embodiment of the present application is not limited by fig. 2 and the following specific sequence, and the mother-infant knowledge topic recommendation method may be implemented by the following steps:
step S210, for each mother and infant commodity, obtaining commodity description information of the mother and infant commodity and a mother and infant knowledge topic library associated with the mother and infant commodity.
In this embodiment, the server 100 stores a mother-infant commodity library S and a mother-infant knowledge topic library D in advance.
In detail, the maternal and infant commodity library S includes all maternal and infant commodities S currently on the shelf of the maternal and infant electronic commerce where the server 100 is located, that is, professional products provided for two specially-related groups, namely, a pregnant woman and a 0-3 year old infant, such as pregnant women, postpartum products, infant bedding products, early education products, nursing products, feeding bottles, auxiliary products, and the like.
The mother-infant knowledge topic master library D comprises various pieces of mother-infant knowledge topic documents D related to inoculation knowledge and inoculation topics, the mother-infant knowledge topic documents can be shared by various users and then uploaded to the server 100, and can also be acquired from other related mother-infant websites and then uploaded to the server 100.
In this embodiment, the commodity feature information of the mother and infant commodity on the current display page may be obtained, and the commodity feature information is used as the commodity description information, where the commodity feature information at least includes a commodity name, a commodity classification, and a commodity title.
Meanwhile, the inventor finds in research that for a common mother-infant e-commerce, the magnitude of a mother-infant commodity library S is hundreds of thousands, the magnitude of a mother-infant knowledge topic library D is hundreds of thousands, and if semantic topic similarity is calculated for each mother-infant commodity and each mother-infant knowledge topic document, D S is a large calculation magnitude. In order to reduce the amount of calculation and reduce unnecessary risks, the embodiment needs to select a smaller mother-infant knowledge topic library with a certain degree of association for each mother-infant commodity s.
Therefore, the mother and infant knowledge topic database related to the mother and infant commodities can be searched based on the commodity characteristic information. In detail, the text corpus in the maternal and infant knowledge topic database D can be put into the open source search engine solr to establish an index, and for each maternal and infant commodity S in the maternal and infant commodity database S, the commodity feature information is used to search out a maternal and infant knowledge topic document set D' related to the commodity from the open source search engine solr, that is, the maternal and infant knowledge topic database related to the maternal and infant commodity. Therefore, the primary relevance between the mother and infant commodities and the alternative document set can be guaranteed, the subsequent calculation amount is reduced, and the performance pressure of the server 100 is reduced.
Step S220, calculating the theme similarity between the commodity description information and each mother-infant knowledge topic document in the mother-infant knowledge topic library to obtain a theme similarity set.
As an embodiment, the step S220 can be implemented by the following sub-steps:
firstly, segmenting each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a plurality of words in each maternal and infant knowledge topic document. For example, if the maternal and infant knowledge topic library D includes 100 maternal and infant knowledge topic documents D, the content in the 100 maternal and infant knowledge topic documents D is segmented.
Then, inputting a plurality of words after word segmentation into an LDA model as LDA model input vectors to train so as to obtain a theme model expression of the mother-infant knowledge topic library, wherein the theme model expression comprises a theme representation of each mother-infant knowledge topic document in the mother-infant knowledge topic library, and the theme representation comprises theme importance degrees of various themes in the mother-infant knowledge topic document. For example, the topic representation of each maternal-infant knowledge topic document may be:
T(d)＝(t1，t2，…，tn)
in the above formula, n is the number of the set subjects, tiThe importance degree of each theme in the mother-infant knowledge topic document d is shown.
The lda (latent Dirichlet allocation) is a document topic generation model, which is also called a three-layer bayesian probability model, and includes three layers of structures, i.e., words, topics, and documents. The generative model means that each word of a maternal-fetal knowledge topic document can be considered to be obtained through a process of "selecting a topic with a certain probability and selecting a word from the topic with a certain probability". The topic documents of the mother-infant knowledge obey polynomial distribution from the theme, and the topic documents of the mother-infant knowledge obey polynomial distribution from the theme to the words. That is, each mother-infant knowledge topic document represents a probability distribution formed by a plurality of topics, and each topic represents a probability distribution formed by a plurality of words.
And then, calculating to obtain the embedded expression of each topic and the embedded expression of each Word by combining a TWE (topic Word embedding) model and the topic model expression.
Then, calculating the probability of generating the commodity description information by each maternal and infant knowledge topic document in the maternal and infant knowledge topic library based on the topic model expression, the embedded expression of each topic and the embedded expression of each word, and taking the probability as the topic similarity between each maternal and infant knowledge topic document and the commodity description information.
Alternatively, the calculation formula of the above steps may be as follows:
wherein Sim (s, d) is subject similarity between the commodity description information s and the mother-infant knowledge topic document d, and P (w | z |)k) As an embedded expression for each word w and each topic zkTopic similarity between embedded expressions of P (z)kD) for each topic zkAnd topic similarity between the embedded expression of (a) and the topic representation of each maternal-fetal knowledge topic document d.
Therefore, when the semantic correlation between one mother-infant commodity s and one mother-infant knowledge topic document d needs to be calculated, because the commodity description information of the mother-infant commodity s is usually a short text, and the mother-infant knowledge topic document d is a long text, when the topic similarity is calculated, the embodiment avoids directly performing topic mapping on the short text, and calculates the probability that each mother-infant knowledge topic document in the mother-infant knowledge topic library generates the commodity description information according to the topic distribution of the mother-infant knowledge topic document d, as the topic similarity between each mother-infant knowledge topic document and the commodity description information.
Step S230, selecting the mother and infant knowledge topic documents meeting the preset rules according to the theme similarity set as recommendation documents of the mother and infant commodities.
As an embodiment, the step S230 can be implemented by the following sub-steps:
firstly, filtering the maternal and infant knowledge topic documents corresponding to the topic similarity smaller than a first preset threshold value in the topic similarity set so as to ensure that the subsequently recommended maternal and infant knowledge topic documents are sufficiently related.
And then, sequencing the rest maternal and infant knowledge topic documents after filtering according to the topic similarity, generating a sequencing result, and selecting the maternal and infant knowledge topic documents meeting a preset rule according to the sequencing result to serve as recommendation documents of the maternal and infant commodities. Optionally, the maternal and infant knowledge topic documents with the topic similarity in the top N number in the ranking result may be selected as the recommendation documents of the maternal and infant commodities; or all mother and infant knowledge topic documents with the topic similarity larger than a second preset threshold value in the sequencing result can be selected as the recommendation documents of the mother and infant commodities.
Therefore, based on the method, the mother and infant knowledge topics related to the mother and infant commodities recommended and browsed for the mother and infant users can be provided when the mother and infant users browse the mother and infant commodities, so that the mother and infant users can know the commodities and the mother and infant knowledge more, the mother and infant users can actively participate in discussion, and commodity contents are generated.
Optionally, referring to fig. 3, after step S230, the mother-infant knowledge topic recommendation method may further include the following steps:
step S240, when it is detected that the product display page of the mother and infant product is opened, displaying the recommendation document of the mother and infant product in the product display page of the mother and infant product.
In this embodiment, when the user browses the product display page of the mother and infant product on the display interface of the user terminal 300, the server 100 displays the recommendation document of the mother and infant product on the product display page of the mother and infant product. Therefore, when browsing the commodity information of the maternal and infant commodity, the user can also select to browse the matched maternal and infant knowledge and maternal and infant topics and participate in discussion, so that the user can further understand the commodity and select a more appropriate maternal and infant commodity.
Referring to fig. 4, a block diagram is schematically shown in a structure of a server 100 for implementing the maternal and infant knowledge topic recommendation method according to an embodiment of the present application. As shown in FIG. 4, the server 100 may be implemented by a bus 110 as a general bus architecture. The bus 110 may include any number of interconnecting buses and bridges depending on the specific application of the server 100 and the overall design constraints. Bus 110 connects various circuits together, including processor 120, storage medium 130, and bus interface 140. Alternatively, the server 100 may connect a network adapter 150 or the like via the bus 110 using the bus interface 140. The network adapter 150 may be used to implement signal processing functions of a physical layer in a wireless communication network and implement transmission and reception of radio frequency signals through an antenna. The user interface 160 may connect external devices such as: a keyboard, a display, a mouse or a joystick, etc. The bus 110 may also connect various other circuits such as timing sources, peripherals, voltage regulators, or power management circuits, which are well known in the art, and therefore, will not be described in detail.
Alternatively, the server 100 may be configured as a general purpose processing system, such as what is commonly referred to as a chip, including: one or more microprocessors providing processing functions, and an external memory providing at least a portion of storage medium 130, all connected together with other support circuits through an external bus architecture.
Alternatively, the server 100 may be implemented using: an ASIC (application specific integrated circuit) having a processor 120, a bus interface 140, a user interface 160; and at least a portion of the storage medium 130 integrated in a single chip, or the server 100 may be implemented using: one or more FPGAs (field programmable gate arrays), PLDs (programmable logic devices), controllers, state machines, gate logic, discrete hardware components, any other suitable circuitry, or any combination of circuitry capable of performing the various functions described throughout this application.
Among other things, processor 120 is responsible for managing bus 110 and general processing (including the execution of software stored on storage medium 130). Processor 120 may be implemented using one or more general-purpose processors and/or special-purpose processors. Examples of processor 120 include microprocessors, microcontrollers, DSP processors, and other circuits capable of executing software. Software should be construed broadly to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
The processor 120 may execute the above embodiments, specifically, the storage medium 130 may store the maternal knowledge topic recommendation device 200 therein, and the processor 120 may be configured to execute the maternal knowledge topic recommendation device 200.
Further, corresponding to the mother-infant knowledge topic recommendation method shown in fig. 2, referring to fig. 3, the mother-infant knowledge topic recommendation device 200 may include:
the obtaining module 210 is configured to obtain, for each mother and infant commodity, commodity description information of the mother and infant commodity and a mother and infant knowledge topic library associated with the mother and infant commodity.
The calculating module 220 is configured to calculate a topic similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a topic similarity set.
And the selecting module 230 is configured to select, according to the theme similarity set, a mother-infant knowledge topic document meeting a preset rule as a recommendation document of the mother-infant commodity.
Optionally, the obtaining module 210 is further configured to obtain commodity feature information of the mother and infant commodity in a current display page, use the commodity feature information as the commodity description information, and retrieve a mother and infant knowledge topic library associated with the mother and infant commodity based on the commodity feature information; the commodity feature information at least comprises a commodity name, a commodity classification and a commodity title.
Optionally, the calculating module 220 is further configured to perform word segmentation on each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a plurality of words in each maternal and infant knowledge topic document;
inputting a plurality of words after word segmentation into an LDA model as LDA model input vectors to train so as to obtain a theme model expression of the mother-infant knowledge topic library, wherein the theme model expression comprises a theme representation of each mother-infant knowledge topic document in the mother-infant knowledge topic library, and the theme representation comprises theme importance degrees of each theme in the mother-infant knowledge topic document;
calculating by combining a TWE model and the topic model expression to obtain an embedded expression of each topic and an embedded expression of each word;
calculating the probability of generating the commodity description information by each mother-infant knowledge topic document in the mother-infant knowledge topic library based on the topic model expression, the embedded expression of each topic and the embedded expression of each word, and taking the probability as the topic similarity between each mother-infant knowledge topic document and the commodity description information.
It can be understood that, for the specific operation method of each functional module in this embodiment, reference may be made to the detailed description of the corresponding step in the foregoing method embodiment, and no repeated description is provided herein.
Further, an embodiment of the present application also provides a readable storage medium, where a computer program is stored, and when the computer program is executed, the method for recommending a mother and infant knowledge topic is implemented.
To sum up, according to the method and the device for recommending the maternal and infant knowledge topics provided by the embodiment of the application, firstly, for each maternal and infant commodity, commodity description information of the maternal and infant commodity and a maternal and infant knowledge topic library associated with the maternal and infant commodity are obtained. And then, calculating the theme similarity between the commodity description information and each mother-infant knowledge topic document in the mother-infant knowledge topic library to obtain a theme similarity set. And finally, selecting the mother-infant knowledge topic documents meeting the preset rules according to the theme similarity set to serve as recommendation documents of the mother-infant commodities. By adopting the technical scheme, the mother and infant knowledge topics related to the mother and infant commodities recommended and browsed for the mother and infant users can be provided when the mother and infant users browse the mother and infant commodities, so that the mother and infant users can know the commodities and the mother and infant knowledge more, the mother and infant users can actively participate in discussion, and commodity contents are generated.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (9)
1. A mother-infant knowledge topic recommendation method is applied to a server and comprises the following steps:
aiming at each mother and infant commodity, acquiring commodity description information of the mother and infant commodity and a mother and infant knowledge question bank related to the mother and infant commodity;
calculating the topic similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a topic similarity set;
selecting mother and infant knowledge topic documents meeting preset rules according to the theme similarity set to serve as recommendation documents of the mother and infant commodities; the content of the mother-infant knowledge topic document is mother-infant knowledge;
and when the opening of the commodity display page of the maternal and infant commodity is detected, displaying the recommendation document of the maternal and infant commodity in the commodity display page of the maternal and infant commodity.
2. The maternal knowledge topic recommendation method according to claim 1, wherein the step of obtaining commodity description information of the maternal and infant commodities and a maternal and infant knowledge topic library associated with the maternal and infant commodities includes:
acquiring commodity feature information of the mother and infant commodity in a current display page, and taking the commodity feature information as commodity description information, wherein the commodity feature information at least comprises a commodity name, a commodity classification and a commodity title;
and retrieving a mother-infant knowledge question bank related to the mother-infant commodity based on the commodity characteristic information.
3. The maternal knowledge topic recommendation method according to claim 1, wherein the step of calculating topic similarity between the commodity description information and each maternal knowledge topic document in the maternal knowledge topic library to obtain a topic similarity set comprises:
segmenting each mother-infant knowledge topic document in the mother-infant knowledge topic library to obtain a plurality of words in each mother-infant knowledge topic document;
inputting a plurality of words after word segmentation into an LDA model as LDA model input vectors to train so as to obtain a theme model expression of the mother-infant knowledge topic library, wherein the theme model expression comprises a theme representation of each mother-infant knowledge topic document in the mother-infant knowledge topic library, and the theme representation comprises theme importance degrees of each theme in the mother-infant knowledge topic document;
calculating by combining a TWE model and the topic model expression to obtain an embedded expression of each topic and an embedded expression of each word;
calculating the probability of generating the commodity description information by each mother-infant knowledge topic document in the mother-infant knowledge topic library based on the topic model expression, the embedded expression of each topic and the embedded expression of each word, and taking the probability as the topic similarity between each mother-infant knowledge topic document and the commodity description information.
4. The maternal knowledge topic recommendation method according to claim 3, wherein the calculation formula for calculating the probability of generating the commodity description information for each maternal knowledge topic document in the maternal knowledge topic library based on the topic model expression, the embedded expression of each topic, and the embedded expression of each word is as follows:
wherein Sim (s, d) is subject similarity between the commodity description information s and the mother-infant knowledge topic document d, and P (w | z |)k) As an embedded expression for each word w and each topic zkTopic similarity between embedded expressions of P (z)kD) for each topic zkAnd topic similarity between the embedded expression of (a) and the topic representation of each maternal-fetal knowledge topic document d.
5. The maternal knowledge topic recommendation method according to claim 1, wherein the step of selecting the maternal knowledge topic documents meeting a preset rule according to the topic similarity set as recommendation documents of the maternal and infant commodities comprises:
filtering mother and infant knowledge topic documents corresponding to the topic similarity smaller than a first preset threshold value in the topic similarity set;
sequencing the rest mother and infant knowledge topic documents after filtering according to the topic similarity, and generating a sequencing result;
and selecting the mother and infant knowledge topic documents meeting the preset rules according to the sequencing result to serve as the recommendation documents of the mother and infant commodities.
6. The maternal knowledge topic recommendation method according to claim 5, wherein the step of selecting the maternal knowledge topic documents meeting the preset rules as the recommendation documents of the maternal and infant commodities according to the sorting results comprises:
selecting the mother and infant knowledge topic documents with the topic similarity in the top N number in the sequencing result as recommendation documents of the mother and infant commodities; or
And selecting all mother and infant knowledge topic documents with the theme similarity larger than a second preset threshold value in the sequencing result as recommendation documents of the mother and infant commodities.
7. An infant and mom knowledge topic recommendation device, which is applied to a server, the device comprising:
the acquisition module is used for acquiring commodity description information of each mother and infant commodity and a mother and infant knowledge question bank related to the mother and infant commodity;
the calculation module is used for calculating the theme similarity between the commodity description information and each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a theme similarity set;
the selecting module is used for selecting the maternal and infant knowledge topic documents meeting the preset rules according to the theme similarity set to serve as the recommendation documents of the maternal and infant commodities, and is used for displaying the recommendation documents of the maternal and infant commodities in the commodity display pages of the maternal and infant commodities when the server detects that the commodity display pages of the maternal and infant commodities are opened;
the contents included in the mother-infant knowledge topic document are mother-infant knowledge.
8. The maternal-infant knowledge topic recommendation device according to claim 7, characterized in that:
the acquisition module is further used for acquiring commodity feature information of the mother and infant commodity in a current display page, taking the commodity feature information as commodity description information, and retrieving a mother and infant knowledge topic library associated with the mother and infant commodity based on the commodity feature information; the commodity feature information at least comprises a commodity name, a commodity classification and a commodity title.
9. The maternal-infant knowledge topic recommendation device according to claim 7, characterized in that:
the calculation module is further used for segmenting each maternal and infant knowledge topic document in the maternal and infant knowledge topic library to obtain a plurality of words in each maternal and infant knowledge topic document;
inputting a plurality of words after word segmentation into an LDA model as LDA model input vectors to train so as to obtain a theme model expression of the mother-infant knowledge topic library, wherein the theme model expression comprises a theme representation of each mother-infant knowledge topic document in the mother-infant knowledge topic library, and the theme representation comprises theme importance degrees of each theme in the mother-infant knowledge topic document;
calculating by combining a TWE model and the topic model expression to obtain an embedded expression of each topic and an embedded expression of each word;
calculating the probability of generating the commodity description information by each mother-infant knowledge topic document in the mother-infant knowledge topic library based on the topic model expression, the embedded expression of each topic and the embedded expression of each word, and taking the probability as the topic similarity between each mother-infant knowledge topic document and the commodity description information.
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