CN110347909A - Products Show method, apparatus, storage medium and electronic equipment - Google Patents
Products Show method, apparatus, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure is directed to a kind of Products Show method, apparatus, storage medium and electronic equipments, belong to intelligent recommendation technical field, this method comprises: obtaining the consulting voice of user, convert text for the consulting voice；The text participle that the consulting voice is converted, obtains each word for forming the text；From each word for forming the text, the tendentiousness product category noun of the user, and the other products class-noun in addition to the tendentiousness product category noun are obtained；It is concentrated from all kinds of daily record datas of the user, obtains the first frequency that the tendentiousness product category noun occurs and the second frequency that the other products class-noun occurs；According to the first frequency and the second frequency, the product category recommended to the user is determined.Consulting voice of the disclosure based on user is analyzed in conjunction with daily record data, effectively improves the accuracy rate of Products Show, improves user experience.
Description
Technical field
This disclosure relates to which intelligent recommendation technical field, is situated between in particular to a kind of Products Show method, apparatus, storage
Matter and electronic equipment.
Background technique
Products Show is exactly to obtain the product category that product client has intention, and then certainly on the platform of product purchase
Trend user recommends such product.
Currently, the program analysis product customer action such as point analysis is usually buried by log on product purchase platform, for example,
By processes such as operation browsing of the analysis product client on purchase platform, most product categories of client's browsing are analyzed,
And then there is the product variety of intention to the automatic recommended products client of product client, but client's navigation process is generally only in nothing
Purpose inquiry, can not the most true purchase intention of representative products client.Traditional product customer behavior analysis usually passes through pair
The log of all operating process of product client is analyzed, and needs to carry out a large amount of statistical work, and process is complicated, recommends simultaneously
Accuracy is lower.
It should be noted that information is only used for reinforcing the reason to the background of the disclosure disclosed in above-mentioned background technology part
Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
The disclosure is designed to provide a kind of Products Show scheme, and then passes through the official communication of user at least to a certain extent
The analysis of voice combination daily record data is ask, the accuracy rate of Products Show is effectively improved, improves user experience.
According to one aspect of the disclosure, a kind of Products Show method is provided, comprising:
The consulting voice for obtaining user, converts text for the consulting voice；
The text participle that the consulting voice is converted, obtains each word for forming the text；
From each word for forming the text, the tendentiousness product category noun of the user is obtained, and except described
Other products class-noun except tendentiousness product category noun；
It is concentrated from all kinds of daily record datas of the user, obtains the first frequency that the tendentiousness product category noun occurs
The second frequency that rate and the other products class-noun occur；
According to the first frequency and the second frequency, the product category recommended to the user is determined.
It is described from each word for forming the text in a kind of exemplary embodiment of the disclosure, obtain the use
The tendentiousness product category noun at family, and the other products class-noun in addition to the tendentiousness product category noun, packet
It includes:
The each word for forming the text is compared with pre-set product class name vocabulary respectively, the production being matched to
Product class-noun；
The text is separated using the product category noun being matched to, obtains multiple text chunks；
According to the word being connected in the text chunk after each product category noun being matched to, described is judged
The tendentiousness product category noun of user described in the product category noun being fitted on；
Other products in the product category noun being matched to described in acquisition in addition to the tendentiousness product category noun
Class-noun.
In a kind of exemplary embodiment of the disclosure, the basis is connected to each product category name being matched to
The word in text chunk after word judges the tendentiousness product category of user described in the product category noun being matched to
Noun, comprising:
From the word in the text chunk being connected to after each product category noun being matched to, obtain with it is each
A corresponding adjective of product category noun being matched to；
By adjective corresponding with each product category noun for being matched to, respectively with preset tendentiousness vocabulary
It is matched, respectively obtains the probability that each corresponding adjective of product category noun being matched to occurs；
According to the tendentiousness product category noun of user described in the probabilistic determination.
In a kind of exemplary embodiment of the disclosure, according to the tendentiousness product category of user described in the probabilistic determination
Noun, comprising:
Obtain a corresponding product category noun being matched to of the maximum probability；
Using one of the maximum probability corresponding product category noun being matched to as the tendency of the user
Property product category noun.
In a kind of exemplary embodiment of the disclosure, from each word for forming the text, obtain the user's
Tendentiousness product category noun, comprising:
From term vector dictionary, the term vector of each word for the text that the composition consulting voice converts is obtained；
The term vector of each word is connected into term vector string；
The term vector string is inputted in trained machine learning model in advance, the tendentiousness product of the user is obtained
Class-noun.
In a kind of exemplary embodiment of the disclosure, the training method of the machine learning model is:
The sample set that user seeks advice from the text that voice converts is collected, the user seeks advice from the text that voice converts
Sample demarcated the tendentiousness product category noun of user in advance；
The conduct input data that all users are seeked advice to the sample for the text that voice converts, inputs machine respectively
Learning model obtains the tendentiousness product class that each user seeks advice from the corresponding user of sample for the text that voice converts
Alias word；
The sample for the text that voice converts, the institute of output are seeked advice from for the user if there is machine learning model
It states the tendentiousness product category noun of user and demarcates in advance inconsistent, then adjust the coefficient of machine learning model, Zhi Daoji
Device learning model for all samples output with sample is demarcated in advance it is consistent, train terminate.
In a kind of exemplary embodiment of the disclosure, according to the first frequency and the second frequency, determine to institute
State the product category of user's recommendation, comprising:
If the first frequency is greater than the second frequency, by the corresponding tendentiousness product category noun of the first frequency
As the product category recommended to the user.
In a kind of exemplary embodiment of the disclosure, the second frequency be it is multiple, according to the first frequency and institute
Second frequency is stated, determines the product category recommended to the user, comprising:
By the first frequency and multiple second frequencies, sort according to descending sequence；
When the first frequency is in the sequence that the sequence sorts, before predetermined rank, by described first
The corresponding tendentiousness product category noun of frequency is as the product category recommended to the user.
According to one aspect of the disclosure, a kind of Products Show device is provided characterized by comprising
Conversion module converts text for the consulting voice for obtaining the consulting voice of user；
Word segmentation module, the text for converting the consulting voice segment, obtain forming the text
Each word；
First obtains module, for obtaining the tendentiousness product class of the user from each word for forming the text
Alias word, and the other products class-noun in addition to the tendentiousness product category noun；
Second obtains module, for concentrating from all kinds of daily record datas of the user, obtains the tendentiousness product category
The second frequency that the first frequency and the other products class-noun that noun occurs occur；
Determining module, for determining the product recommended to the user according to the first frequency and the second frequency
Classification.
According to one aspect of the disclosure, a kind of computer readable storage medium is provided, Products Show journey is stored thereon with
Sequence, which is characterized in that the Products Show program realizes method described in any of the above embodiments when being executed by processor.
According to one aspect of the disclosure, a kind of electronic equipment is provided characterized by comprising
Processor；And
Memory, for storing the Products Show program of the processor；Wherein, the processor is configured to via execution
The Products Show program executes method described in any of the above embodiments.
A kind of Products Show method and device of the disclosure turns the consulting voice firstly, obtaining the consulting voice of user
Turn to text；After the consulting voice with contact staff by obtaining user, it is converted into text, it so in the next steps, can
Most significantly to seek advice from the text that voice converts based on expression user's purchase intention, the intention of user is accurately analyzed.
The text participle that the consulting voice is converted, obtains each word for forming the text；It can by text participle
It is divided into different words with the text for converting voice, thus can accurately obtains the word for user intent judgement.From group
At the tendentiousness product category noun in each word of the text, obtaining the user, and remove the tendentiousness product class
Other products class-noun except alias word；Tendentiousness product category noun by obtaining user can be accurately obtained
User seeks advice from the product variety in voice with purchase intention, and then in the next steps, can to combine other client's intentions,
The accurately intention of analysis client.It is concentrated from all kinds of daily record datas of the user, obtains the tendentiousness product category noun and go out
The second frequency that existing first frequency and the other products class-noun occur；First frequency can be with reactor product client
Intention degree of the product category with intention in voice in all kinds of logs of client is seeked advice from, while second frequency can react
Intention degree of other product categories in all kinds of logs of client out, and then can accurately judge that client is true in subsequent step
Just with the product category of purchase intention.Recommended according to the first frequency and the second frequency, determination to the user
Product category；By first frequency and second frequency comparative analysis, the corresponding tendentiousness product category of available first frequency
Tendentiousness of the noun in all kinds of daily record datas of user, and then simply, efficiently and accurately determine to user recommendation
Product category.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The disclosure can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure
Example, and together with specification for explaining the principles of this disclosure.It should be evident that the accompanying drawings in the following description is only the disclosure
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.
Fig. 1 schematically shows a kind of flow chart of Products Show method.
Fig. 2 schematically shows a kind of Application Scenarios-Example figure of Products Show method.
Fig. 3 schematically shows a kind of method flow diagram of tendentiousness product category noun judgement.
Fig. 4 schematically shows a kind of block diagram of Products Show device.
Fig. 5 schematically shows a kind of electronic equipment example block diagram for realizing the said goods recommended method.
Fig. 6 schematically shows a kind of computer readable storage medium for realizing the said goods recommended method.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be with a variety of shapes
Formula is implemented, and is not understood as limited to example set forth herein；On the contrary, thesing embodiments are provided so that the disclosure will more
Fully and completely, and by the design of example embodiment comprehensively it is communicated to those skilled in the art.Described feature, knot
Structure or characteristic can be incorporated in any suitable manner in one or more embodiments.In the following description, it provides perhaps
More details fully understand embodiment of the present disclosure to provide.It will be appreciated, however, by one skilled in the art that can
It is omitted with technical solution of the disclosure one or more in the specific detail, or others side can be used
Method, constituent element, device, step etc..In other cases, be not shown in detail or describe known solution to avoid a presumptuous guest usurps the role of the host and
So that all aspects of this disclosure thicken.
In addition, attached drawing is only the schematic illustrations of the disclosure, it is not necessarily drawn to scale.Identical attached drawing mark in figure
Note indicates same or similar part, thus will omit repetition thereof.Some block diagrams shown in the drawings are function
Energy entity, not necessarily must be corresponding with physically or logically independent entity.These function can be realized using software form
Energy entity, or these functional entitys are realized in one or more hardware modules or integrated circuit, or at heterogeneous networks and/or place
These functional entitys are realized in reason device device and/or microcontroller device.
Products Show method is provided firstly in this example embodiment, the service which can run on
Device can also run on server cluster or Cloud Server etc., and certainly, those skilled in the art can also be according to demand at other
Platform runs method of the invention, and particular determination is not done to this in the present exemplary embodiment.Refering to what is shown in Fig. 1, the Products Show
Method may comprise steps of:
Step S110 obtains the consulting voice of user, converts text for the consulting voice；
The text that the consulting voice converts is segmented, obtains forming each of the text by step S120
Word；
Step S130 obtains the tendentiousness product category noun of the user from each word for forming the text, with
And the other products class-noun in addition to the tendentiousness product category noun；
Step S140 concentrates from all kinds of daily record datas of the user of the product, obtains the tendentiousness product category noun
The second frequency that the first frequency of appearance and the other products class-noun occur；
Step S150 determines the product category recommended to the user according to the first frequency and the second frequency.
In the said goods recommended method, firstly, obtaining the consulting voice of user, text is converted by the consulting voice；
After the consulting voice with contact staff by obtaining user, it is converted into text, it so in the next steps, can be based on expression
Most significantly consulting converts obtained text to user's purchase intention, accurately analyzes the intention of user.By the consulting voice
Obtained text participle is converted, each word for forming the text is obtained；Voice can be converted by text participle
Text is divided into different words, thus can accurately obtain the word for user intent judgement.From each of the composition text
In a word, the tendentiousness product category noun of the user is obtained, and its in addition to the tendentiousness product category noun
Its product category noun；Tendentiousness product category noun by obtaining user can be accurately obtained user and seek advice from voice
Product variety with purchase intention, and then in the next steps, client can be accurately analyzed to combine other client's intentions
Intention.It is concentrated from all kinds of daily record datas of the user, obtains the first frequency that the tendentiousness product category noun occurs,
And the second frequency that the other products class-noun occurs；First frequency can seek advice from the tool in voice with reactor product client
Intention degree of the product category having intention in all kinds of logs of client, while second frequency can reflect other product categories
Intention degree in all kinds of logs of client, and then can accurately judge that client really has purchase intention in subsequent step
Product category.According to the first frequency and the second frequency, the product category recommended to the user is determined；Pass through
One frequency and second frequency comparative analysis, the corresponding tendentiousness product category noun of available first frequency is in all kinds of of user
Tendentiousness in daily record data, and then simply, efficiently and accurately determine the product category recommended to the user.
In the following, will be carried out in conjunction with attached drawing to each step in this example embodiment in the said goods recommended method detailed
Explanation and explanation.
In step s 110, the consulting voice is converted text by the consulting voice for obtaining user.
In this exemplary embodiment, refering to what is shown in Fig. 2, server 201 obtains the user being stored in server 202
Consulting voice, then by speech recognition by seek advice from voice be converted into text.Wherein, server 201 can be and any have
The terminal executed program instructions, such as mobile phone, computer etc.；Terminal device 202 can be any terminal with store function, example
Such as mobile phone, computer.Wherein, the consulting voice that server 201 obtains the user being stored in server 202, which can be, directly climbs
The mode taken obtains, and after being also possible to server 201 by sending acquisition instruction, the side of consulting voice is returned to by server 202
Formula does not do particular determination herein.
On product purchase platform, user is when needing to buy product, it will usually various types of other product is inquired, with inquiry
To customer satisfaction system insurance products.Usually these operation be all by the progress such as manual search, but when user query arrive oneself
Oneself more satisfied product, alternatively, when needing further to understand the product of certain classification, usually by with contact staff's
Depth consulting is understood.The main behavior meaning of product client can be directly and accurately recognized by the consulting voice of user
Figure, and then can be in the next steps around the demand of main behavior purposes analysis user, that is, the production with purchase intention
The other analysis of category.Meanwhile by converting text for voice, so that it may be carried out in subsequent analysis step by content of text
Accurately analysis.
In the step s 120, the text that the consulting voice converts is segmented, obtains forming the text
Each word.
In this exemplary embodiment, accurately text can be divided into using existing text segmenting method different
Vocabulary, for example, by trained segmentation methods can will " I want to know about children danger " be divided into " I " " desired " "
Solution " " once " " children danger " etc.；The word for needing classification is accurately found in the vocabulary that can be obtained after participle in this way, for example,
Product category vocabulary " children danger ", and then the behavior that can accurately analyze user in the next steps is intended to.
In step s 130, from each word for forming the text, the tendentiousness product category name of the user is obtained
Word, and the other products class-noun in addition to the tendentiousness product category noun；
In this exemplary embodiment, the tendentiousness product category noun of user is exactly the production that user has purchase intention
The title of product kind, for example, children danger or children danger third class set meal.The voice reference content of user is to react user most
The content of strong purchase intention is comprising the product category noun of user's tendency.By to each in the text after participle
The part of speech analysis of word can find out the vocabulary of such as nouns and adjectives, can accurately can find for example, by noun template
Product noun therein, the template for example, by being inclined to sexual behaviour word can accurately judge the tendentiousness of user.From composition institute
It states in each word of text, that is, seeks advice from each word in the text that voice converts, the tendentiousness for obtaining user produces
Product class-noun, so that it may obtain user's product category with purchase intention in consulting voice.It obtains and removes the tendentiousness
Other products class-noun except product category noun, other product classes that available client is understood by consulting voice
Not, that is, user also has product category of interest.It can be effectively ensured in this way based on the user behavior analysis of consulting voice
The accuracy rate of the true purchase intention judgement of client is carried out in the next steps.
It is described from each word for forming the text in a kind of originally exemplary embodiment, obtain the user's
Tendentiousness product category noun, and the other products class-noun in addition to the tendentiousness product category noun, comprising:
The each word for forming the text is compared with pre-set product class name vocabulary respectively, the production being matched to
Product class-noun；
The text is separated using the product category noun being matched to, obtains multiple text chunks；
According to the word being connected in the text chunk after each product category noun being matched to, described is judged
The tendentiousness product category noun of user described in the product category noun being fitted on；
Other products in the product category noun being matched to described in acquisition in addition to the tendentiousness product category noun
Class-noun.
Pre-set product class name vocabulary is exactly the noun comprising all product categories, can accurately, just using the table
The product category noun for including in each word for the text that consulting voice converts is found out promptly.Meanwhile generally directed to each
The description of product category noun, that is, user is to the inherent emotion tendency of each product category, just in table followed by
In stating.The text is separated using product category noun, obtains multiple text chunks, so that it may in the next steps, be utilized
Description to each product category noun, accurately judge user to the tendentiousness of each product category noun, and then accurately
The tendentiousness product category noun of user is obtained, while being got in the product category noun being matched to except tendentiousness product category
Other products class-noun except noun.
In a kind of this exemplary embodiment, the basis be connected to each product category noun being matched to it
The word in text chunk afterwards judges the tendentiousness product category name of user described in the product category noun being matched to
Word, refering to what is shown in Fig. 3, the following steps are included:
Step S310, the word from the text chunk being connected to after each product category noun being matched to
In, obtain adjective corresponding with each product category noun for being matched to；
Step S320 inclines adjective corresponding with each product category noun for being matched to preset respectively
Tropism vocabulary is matched, and the general of each corresponding adjective appearance of product category noun being matched to is respectively obtained
Rate；
Step S330, according to the tendentiousness product category noun of user described in the probabilistic determination.
Usual adjective is the vocabulary represented to one section of content most psychological activity tendency, by obtain with it is each described
The corresponding adjective of product category noun being matched to can accurately judge product client to the true of each product category noun
Real tendentiousness.Preset tendentiousness vocabulary includes the different adjectives and corresponding tendency that historic customer is stated
Property, for example, it is hunky-dory just represent client there is certain tendency purchase, very good just represent has tendency to buy very much.By will be with
Each corresponding adjective of product category noun being matched to, is matched with preset tendentiousness vocabulary respectively, point
The probability that each corresponding adjective of product category noun being matched to occurs in preset tendentiousness vocabulary is not obtained,
Thus product client can accurately be determined to the satisfaction of a product category noun by the height of probability, that is,
Tendentiousness, and then accurately judge the tendentiousness product category noun of user.
In a kind of originally exemplary embodiment, according to the tendentiousness product category name of user described in the probabilistic determination
Word, comprising:
Obtain a corresponding product category noun being matched to of the maximum probability；
Using one of the maximum probability corresponding product category noun being matched to as the tendency of the user
Property product category noun.
The corresponding product category noun being matched to of one of maximum probability is exactly the highest production of tendentiousness of user
Tendentiousness product category noun can be effectively ensured using the word as the tendentiousness product category noun of user in product class-noun
The accuracy of judgement.
In a kind of originally exemplary embodiment, from each word for forming the text, the tendency of the user is obtained
Property product category noun, comprising:
From term vector dictionary, the term vector of each word for the text that the composition consulting voice converts is obtained；
The term vector of each word is connected into term vector string；
The term vector string is inputted in trained machine learning model in advance, the tendentiousness product of the user is obtained
Class-noun.
The each word for the text that consulting voice converts can accurately express the meaning of consulting voice, such as " I wants
Learn about children danger " be divided into " I " " desired " " understanding " " once " " children dangerous " after, about product category noun and correspondence
Vocabulary have independent vocabulary, rather than single word.Term vector dictionary is exactly each word and corresponding unique term vector
Queries dictionary.The term vector of each word is obtained, term vector string is then connected into, it can be in the feelings that consulting voice semanteme is effectively ensured
Term vector string input machine learning model can accurately and efficiently be judged to consult by trained machine learning model under condition
Ask the tendentiousness product category noun of user in each word for the text that voice converts.
In a kind of originally exemplary embodiment, the training method of the machine learning model is:
The sample set that user seeks advice from the text that voice converts is collected, the user seeks advice from the text that voice converts
Sample demarcated the tendentiousness product category noun of user in advance；
The conduct input data that all users are seeked advice to the sample for the text that voice converts, inputs machine respectively
Learning model obtains the tendentiousness product class that each user seeks advice from the corresponding user of sample for the text that voice converts
Alias word；
The sample for the text that voice converts, the institute of output are seeked advice from for the user if there is machine learning model
It states the tendentiousness product category noun of user and demarcates in advance inconsistent, then adjust the coefficient of machine learning model, Zhi Daoji
Device learning model for all samples output with sample is demarcated in advance it is consistent, train terminate.
The each word for the text that consulting voice converts can accurately express the meaning of consulting voice, such as " I wants
Learn about children danger " be divided into " I " " desired " " understanding " " once " " children dangerous " after, about product category noun and correspondence
Vocabulary have independent vocabulary, rather than single word.The conduct input number of the sample for the text that consulting voice converts
According to be exactly seek advice from the text that voice converts each word after, obtain the term vector of each word, be then connected into term vector string.
It can guarantee the semantic accurate of the text of consulting voice in this way, and then the accurate of machine learning model training is effectively ensured
Property.
In step S140, is concentrated from all kinds of daily record datas of the user, obtain the tendentiousness product category noun and go out
The second frequency that existing first frequency and the other products class-noun occur；
It in this exemplary embodiment, is concentrated from all kinds of daily record datas of user, obtains tendentiousness product category noun and go out
Existing first frequency, first frequency are exactly tendentiousness product category noun corresponding product class of the product client in consulting voice
The probability occurred is not concentrated in the search log data set of such as user, collection daily record data, the probability the high more to illustrate to produce
The true purchase intention of product client.Obtain that other products class-noun except tendentiousness product category noun occurs simultaneously the
Two frequencies are exactly the search log data set of such as user, and collection daily record data concentrates the product category name of other product varietys
The probability that word occurs；Client be can reflect to the intention degree of the product of other classifications.In turn, in conjunction with first frequency and second
Frequency can accurately judge client to the intention degree of the product of the corresponding product category of first frequency.
In step S150, according to the first frequency and the second frequency, the product recommended to the user is determined
Classification.
In this exemplary embodiment, it can accurately judge client to the first frequency in conjunction with first frequency and second frequency
The intention degree of the corresponding product category of rate.Such as first frequency very higher position illustrate user voice consulting and operation log in
All there is very high intention, and then can accurately recommend the product of the category to user.First frequency pair available in this way
Tendentiousness of the tendentiousness product category noun answered in all kinds of daily record datas of user, and then simply, efficiently and accurately determine
The product category recommended out to the user.
In a kind of originally exemplary embodiment, according to the first frequency and the second frequency, determine to the use
The product category that family is recommended, comprising:
If the first frequency is greater than the second frequency, by the corresponding tendentiousness product category noun of the first frequency
As the product category recommended to the user.
First frequency very higher position illustrates that user's all has highest intention, Jin Erke in voice consulting and operation log
Accurately to recommend the product of the category to user.
In a kind of this exemplary embodiment, the second frequency be it is multiple, according to the first frequency and described the
Two frequencies determine the product category recommended to the user, comprising:
By the first frequency and multiple second frequencies, sort according to descending sequence；
When the first frequency is in the sequence that the sequence sorts, before predetermined rank, by described first
The corresponding tendentiousness product category noun of frequency is as the product category recommended to the user.
By first frequency and multiple second frequencies, sort according to descending sequence, available first frequency
Ranking.Then, the ranking of first frequency is located at before predetermined rank in the ranking of all probability, for example, one shares 10 generally
Rate ranking, the first probability come front three, illustrate the purchase intention of the corresponding product category of the first probability in voice consulting and
There is higher intention degree in operation log, and then accurately can recommend the corresponding tendentiousness product of first frequency to user
Classification.
The disclosure additionally provides a kind of Products Show device.Refering to what is shown in Fig. 4, the Products Show device may include conversion
Module 410, word segmentation module 420, first obtain module 430, second and obtain module 440 and determining module 450.Wherein:
Conversion module 410 can be used for obtaining the consulting voice of user, convert text for the consulting voice；
Word segmentation module 420 can be used for the text participle for converting the consulting voice, obtain described in composition
Each word of text；
First acquisition module 430 can be used for from each word for forming the text, obtain the tendentiousness of the user
Product category noun, and the other products class-noun in addition to the tendentiousness product category noun；
Second acquisition module 440 can be used for concentrating from all kinds of daily record datas of the user, obtain the tendentiousness product
The second frequency that the first frequency and the other products class-noun that class-noun occurs occur；
Determining module 450 can be used for determining and recommending to the user according to the first frequency and the second frequency
Product category.
The detail of each module has carried out in corresponding Products Show method in detail in the said goods recommendation apparatus
Thin description, therefore details are not described herein again.
It should be noted that although being referred to several modules or list for acting the equipment executed in the above detailed description
Member, but this division is not enforceable.In fact, according to embodiment of the present disclosure, it is above-described two or more
Module or the feature and function of unit can embody in a module or unit.Conversely, an above-described mould
The feature and function of block or unit can be to be embodied by multiple modules or unit with further division.
In addition, although describing each step of method in the disclosure in the accompanying drawings with particular order, this does not really want
These steps must be executed in this particular order by asking or implying, or having to carry out step shown in whole could realize
Desired result.Additional or alternative, it is convenient to omit multiple steps are merged into a step and executed by certain steps, and/
Or a step is decomposed into execution of multiple steps etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, mobile terminal or network equipment etc.) is executed according to disclosure embodiment
Method.
In an exemplary embodiment of the disclosure, a kind of electronic equipment that can be realized the above method is additionally provided.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be embodied in the following forms, it may be assumed that complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as circuit, " module " or " system ".
The electronic equipment 500 of this embodiment according to the present invention is described referring to Fig. 5.The electronics that Fig. 5 is shown
Equipment 500 is only an example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in figure 5, electronic equipment 500 is showed in the form of universal computing device.The component of electronic equipment 500 can wrap
It includes but is not limited to: at least one above-mentioned processing unit 510, at least one above-mentioned storage unit 520, the different system components of connection
The bus 530 of (including storage unit 520 and processing unit 510).
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 510
Row, so that various according to the present invention described in the execution of the processing unit 510 above-mentioned " illustrative methods " part of this specification
The step of illustrative embodiments.For example, the processing unit 510 can execute step S110 as shown in fig. 1: obtaining and use
The consulting voice is converted text by the consulting voice at family；S120: the text point that the consulting voice is converted
Word obtains each word for forming the text；Step S130: from each word for forming the text, obtain the user's
Tendentiousness product category noun, and the other products class-noun in addition to the tendentiousness product category noun；Step
S140: concentrating from all kinds of daily record datas of the user, obtains the first frequency that the tendentiousness product category noun occurs, and
The second frequency that the other products class-noun occurs；Step S150: according to the first frequency and the second frequency, really
Orient the product category that the user recommends.
Storage unit 520 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 5201 and/or cache memory unit 5202, it can further include read-only memory unit (ROM) 5203.
Storage unit 520 can also include program/utility with one group of (at least one) program module 5205
5204, such program module 5205 includes but is not limited to: operating system, one or more application program, other program moulds
It may include the realization of network environment in block and program data, each of these examples or certain combination.
Bus 530 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures
Local bus.
Electronic equipment 500 can also be with one or more external equipments 700 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, the equipment that also client can be enabled interact with the electronic equipment 500 with one or more communicates, and/or with make
Any equipment (such as the router, modulation /demodulation that the electronic equipment 500 can be communicated with one or more of the other calculating equipment
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 550.Also, electronic equipment 500 can be with
By network adapter 560 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.As shown, network adapter 560 is communicated by bus 530 with other modules of electronic equipment 500.
It should be understood that although not shown in the drawings, other hardware and/or software module can not used in conjunction with electronic equipment 500, including but not
Be limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and
Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, terminal installation or network equipment etc.) is executed according to disclosure embodiment
Method.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is additionally provided, energy is stored thereon with
Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention may be used also
In the form of being embodied as a kind of program product comprising program code, when described program product is run on the terminal device, institute
Program code is stated for executing the terminal device described in above-mentioned " illustrative methods " part of this specification according to this hair
The step of bright various illustrative embodiments.
Refering to what is shown in Fig. 6, describing the program product for realizing the above method of embodiment according to the present invention
600, can using portable compact disc read only memory (CD-ROM) and including program code, and can in terminal device,
Such as it is run on PC.However, program product of the invention is without being limited thereto, in this document, readable storage medium storing program for executing can be with
To be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
It is in connection.
Described program product can be using any combination of one or more readable mediums.Readable medium can be readable letter
Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or
System, device or the device of semiconductor, or any above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive
List) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing
Matter, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or and its
The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have
Line, optical cable, RF etc. or above-mentioned any appropriate combination.
The program for executing operation of the present invention can be write with any combination of one or more programming languages
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in client
It calculates and executes in equipment, partly executes on the client device, being executed as an independent software package, partially in client's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to client computing device, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In addition, above-mentioned attached drawing is only the schematic theory of processing included by method according to an exemplary embodiment of the present invention
It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable
Sequence.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure
His embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or
Adaptive change follow the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure or
Conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by claim
It points out.
Claims (10)
1. a kind of Products Show method characterized by comprising
The consulting voice for obtaining user, converts text for the consulting voice；
The text participle that the consulting voice is converted, obtains each word for forming the text；
From each word for forming the text, the tendentiousness product category noun of the user is obtained, and remove the tendency
Other products class-noun except property product category noun；
It is concentrated from all kinds of daily record datas of the user, obtains the first frequency that the tendentiousness product category noun occurs, with
And the second frequency that the other products class-noun occurs；
According to the first frequency and the second frequency, the product category recommended to the user is determined.
2. the method according to claim 1, wherein described from each word for forming the text, acquisition institute
State the tendentiousness product category noun of user, and the other products class name in addition to the tendentiousness product category noun
Word, comprising:
The each word for forming the text is compared with pre-set product class name vocabulary respectively, the product class being matched to
Alias word；
The text is separated using the product category noun being matched to, obtains multiple text chunks；
According to the word being connected in the text chunk after each product category noun being matched to, described be matched to is judged
Product category noun described in user tendentiousness product category noun；
Other products classification in the product category noun being matched to described in acquisition in addition to the tendentiousness product category noun
Noun.
3. according to the method described in claim 2, it is characterized in that, the basis is connected to each product class being matched to
The word in text chunk after alias word judges the tendentiousness product of user described in the product category noun being matched to
Class-noun, comprising:
From the word in the text chunk being connected to after each product category noun being matched to, obtain and each institute
State the corresponding adjective of product category noun being matched to；
By adjective corresponding with each product category noun for being matched to, carried out respectively with preset tendentiousness vocabulary
Matching respectively obtains the probability that each corresponding adjective of product category noun being matched to occurs；
According to the tendentiousness product category noun of user described in the probabilistic determination.
4. according to the method described in claim 3, it is characterized in that, the tendentiousness of the user according to the probabilistic determination
Product category noun, comprising:
Obtain a corresponding product category noun being matched to of the maximum probability；
It is produced using one of the maximum probability corresponding product category noun being matched to as the tendentiousness of the user
Product class-noun.
5. the method according to claim 1, wherein described from each word for forming the text, acquisition institute
State the tendentiousness product category noun of user, comprising:
From term vector dictionary, the term vector of each word for the text that the composition consulting voice converts is obtained；
The term vector of each word is connected into term vector string；
The term vector string is inputted in trained machine learning model in advance, the tendentiousness product category of the user is obtained
Noun.
6. according to the method described in claim 5, it is characterized in that, the training method of the machine learning model is:
The sample set that user seeks advice from the text that voice converts is collected, the user seeks advice from the sample for the text that voice converts
This has demarcated the tendentiousness product category noun of user in advance；
The conduct input data that all users are seeked advice to the sample for the text that voice converts, inputs machine learning respectively
Model obtains the tendentiousness product category name that each user seeks advice from the corresponding user of sample for the text that voice converts
Word；
The sample for the text that voice converts, the use of output are seeked advice from for the user if there is machine learning model
The tendentiousness product category noun at family with demarcate in advance inconsistent, then the coefficient of machine learning model is adjusted, until engineering
Practise model for all samples output with sample is demarcated in advance it is consistent, train terminate.
7. the method according to claim 1, wherein described according to the first frequency and the second frequency,
Determine the product category recommended to the user, comprising:
If the first frequency be greater than the second frequency, using the corresponding tendentiousness product category noun of the first frequency as
The product category recommended to the user.
8. a kind of Products Show device characterized by comprising
Conversion module converts text for the consulting voice for obtaining the consulting voice of user；
Word segmentation module, the text for converting the consulting voice segment, obtain forming each of the text
Word；
First obtains module, for obtaining the tendentiousness product category name of the user from each word for forming the text
Word, and the other products class-noun in addition to the tendentiousness product category noun；
Second obtains module, for concentrating from all kinds of daily record datas of the user, obtains the tendentiousness product category noun
The second frequency that the first frequency of appearance and the other products class-noun occur；
Determining module, for determining the product category recommended to the user according to the first frequency and the second frequency.
9. a kind of computer readable storage medium is stored thereon with Products Show program, which is characterized in that the Products Show journey
Claim 1-7 described in any item methods are realized when sequence is executed by processor.
10. a kind of electronic equipment characterized by comprising
Processor；And
Memory, for storing the Products Show program of the processor；Wherein, the processor is configured to via described in execution
Products Show program carrys out perform claim and requires the described in any item methods of 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910435038.4A CN110347909A (en) | 2019-05-23 | 2019-05-23 | Products Show method, apparatus, storage medium and electronic equipment |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910435038.4A CN110347909A (en) | 2019-05-23 | 2019-05-23 | Products Show method, apparatus, storage medium and electronic equipment |
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| Publication Number | Publication Date |
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| CN110347909A true CN110347909A (en) | 2019-10-18 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021218339A1 (en) * | 2020-04-28 | 2021-11-04 | 深圳壹账通智能科技有限公司 | Artificial intelligence-based topic mining method and apparatus, electronic device and medium |
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2019
- 2019-05-23 CN CN201910435038.4A patent/CN110347909A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021218339A1 (en) * | 2020-04-28 | 2021-11-04 | 深圳壹账通智能科技有限公司 | Artificial intelligence-based topic mining method and apparatus, electronic device and medium |
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