CN115187345A - Intelligent household building material recommendation method, device, equipment and storage medium - Google Patents
Intelligent household building material recommendation method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses an intelligent home building material recommendation method, device, equipment and storage medium, which are used for improving the accuracy of intelligent home building material recommendation. The method comprises the following steps: obtaining conversation data of a target user, and performing data extraction on the conversation data to obtain voice data and text data; performing voice feature extraction on voice data to obtain first feature information, and generating second feature information according to text data; performing preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features; calling an intelligent recommendation model to calculate a target push path corresponding to the push preference feature; generating a home building material list to be pushed according to the target pushing path and the pushing preference characteristics; and generating a target home building material recommendation sequence according to the home building material list to be pushed, and recommending the target user according to the target home building material recommendation sequence.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an intelligent household building material recommendation method, device, equipment and storage medium.
Background
At present, with the high-speed development of the internet of things technology, the update iteration speed of the smart home building materials is also high, so in order to facilitate recommendation of the smart home building materials, merchants generally recommend the commodities of users by establishing a data list of the smart home building materials. When a user purchases an intelligent home building material product, the user usually selects to go to a physical store for experience and feeling, and a merchant cannot provide quick and accurate commodity service for the user because the intelligent home building material has numerous types and high iteration speed.
According to the existing scheme, the intelligent home building materials are recommended manually, but a user needs to consume a large amount of time to select, the intelligent home building materials needed by the user cannot be recommended for the user quickly and accurately, and the accuracy of the existing scheme is low.
Disclosure of Invention
The invention provides an intelligent household building material recommendation method, device, equipment and storage medium, which are used for improving the accuracy of intelligent household building material recommendation.
The invention provides an intelligent household building material recommendation method in a first aspect, which comprises the following steps: the method comprises the steps that conversation data of a target user are obtained based on a preset intelligent question-answering terminal, and data extraction is carried out on the conversation data to obtain voice data and text data; performing voice feature extraction on the voice data to obtain first feature information of the target user, and generating second feature information of the target user according to the text data; performing preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features of the target user; calling a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature; generating a home building material list to be pushed corresponding to the target user according to the target pushing path and the pushing preference characteristics; and generating a target home building material recommendation sequence according to the home building material list to be pushed, and recommending the target user according to the target home building material recommendation sequence.
Optionally, in a first implementation manner of the first aspect of the present invention, the performing voice feature extraction on the voice data to obtain first feature information of the target user, and generating second feature information of the target user according to the text data includes: performing voiceprint feature extraction on the voice data to obtain a voiceprint feature corresponding to the target user; extracting a plurality of first feature elements from the voiceprint features to obtain a plurality of first feature elements, and generating first feature information of the target user according to the plurality of first feature elements; and extracting a plurality of second characteristic elements from the text data to obtain a plurality of second characteristic elements, and generating second characteristic information of the target user according to the plurality of second characteristic elements.
Optionally, in a second implementation manner of the first aspect of the present invention, the performing, according to the first feature information and the second feature information, a preference feature analysis on the target user to obtain a push preference feature of the target user includes: extracting a plurality of first feature elements in the first feature information and extracting a plurality of second feature elements in the second feature information; constructing a user feature set based on the plurality of first feature elements and the plurality of second feature elements; and carrying out preference characteristic analysis on the user characteristic set to obtain the push preference characteristic of the target user.
Optionally, in a third implementation manner of the first aspect of the present invention, the invoking a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature includes: calling a preset intelligent recommendation model to perform pushing path configuration on the pushing preference characteristics to obtain a pushing path configuration result; performing result data analysis on the configuration result of the push path, and generating a plurality of candidate push paths corresponding to the target user; carrying out pushing path probability calculation on the candidate pushing paths to obtain a probability value corresponding to each candidate pushing path; and determining a target push path according to the probability value corresponding to each candidate push path.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the generating a list of home building materials to be pushed, which corresponds to the target user, according to the target push path and the push preference feature includes: performing path node analysis on the target pushing path to obtain path node data; calling a preset database to perform clustering processing on the path node data to obtain a clustering result corresponding to the path node data; and establishing a home building material list to be pushed corresponding to the target user according to the clustering result.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the generating a target home building material recommendation sequence according to the home building material list to be pushed, and recommending the target user according to the target home building material recommendation sequence includes: generating a target home building material recommendation sequence according to the home building material list to be pushed; extracting user real-time behavior data corresponding to the target user from the database; and selecting at least two home building materials to be recommended from the target home building material recommendation sequence according to the real-time behavior data of the user, and recommending the at least two home building materials to be recommended to the target user.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the method for recommending smart home building materials further includes: acquiring feedback data of the target user after receiving the push; and sequentially adjusting the target home building material recommendation sequence according to the feedback data.
The invention provides an intelligent home building material recommendation device, which comprises: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring dialogue data of a target user based on a preset intelligent question-answering terminal and extracting the data of the dialogue data to obtain voice data and text data; the extraction module is used for extracting voice characteristics of the voice data to obtain first characteristic information of the target user and generating second characteristic information of the target user according to the text data; the analysis module is used for carrying out preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features of the target user; the calculation module is used for calling a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature; the generation module is used for generating a home building material list to be pushed corresponding to the target user according to the target pushing path and the pushing preference characteristics; and the recommendation module is used for generating a target home building material recommendation sequence according to the home building material list to be pushed and recommending the target user according to the target home building material recommendation sequence.
Optionally, in a first implementation manner of the second aspect of the present invention, the extracting module is specifically configured to: voice print feature extraction is carried out on the voice data, and voice print features corresponding to the target user are obtained; extracting a plurality of first feature elements from the voiceprint features to obtain a plurality of first feature elements, and generating first feature information of the target user according to the plurality of first feature elements; and extracting a plurality of second characteristic elements from the text data to obtain a plurality of second characteristic elements, and generating second characteristic information of the target user according to the plurality of second characteristic elements.
Optionally, in a second implementation manner of the second aspect of the present invention, the analysis module is specifically configured to: extracting a plurality of first feature elements in the first feature information and extracting a plurality of second feature elements in the second feature information; constructing a user feature set based on the plurality of first feature elements and the plurality of second feature elements; and carrying out preference characteristic analysis on the user characteristic set to obtain the push preference characteristic of the target user.
Optionally, in a third implementation manner of the second aspect of the present invention, the calculating module is specifically configured to: calling a preset intelligent recommendation model to carry out push path configuration on the push preference characteristics to obtain a push path configuration result; performing result data analysis on the configuration result of the push path, and generating a plurality of candidate push paths corresponding to the target user; carrying out pushing path probability calculation on the candidate pushing paths to obtain a probability value corresponding to each candidate pushing path; and determining a target push path according to the probability value corresponding to each candidate push path.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the generating module is specifically configured to: performing path node analysis on the target pushing path to obtain path node data; calling a preset database to perform clustering processing on the path node data to obtain a clustering result corresponding to the path node data; and creating a home building material list to be pushed corresponding to the target user according to the clustering result.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the recommending module is specifically configured to: generating a target home building material recommendation sequence according to the home building material list to be pushed; extracting user real-time behavior data corresponding to the target user from the database; and selecting at least two home building materials to be recommended from the target home building material recommendation sequence according to the real-time behavior data of the user, and recommending the at least two home building materials to be recommended to the target user.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the smart home building material recommendation device further includes: the adjusting module is used for acquiring feedback data after the target user receives the push; and sequentially adjusting the target home building material recommendation sequence according to the feedback data.
The third aspect of the invention provides an intelligent household building material recommendation device, which comprises: a memory and at least one processor, the memory having instructions stored therein; the at least one processor calls the instruction in the memory, so that the intelligent home building material recommending device executes the intelligent home building material recommending method.
A fourth aspect of the present invention provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the above-mentioned smart home building material recommendation method.
In the technical scheme provided by the invention, the dialogue data of a target user is obtained, and the data extraction is carried out on the dialogue data to obtain voice data and text data; voice feature extraction is carried out on voice data to obtain first feature information, second feature information is generated according to text data, and the intention analysis accuracy of a target user is improved by analyzing the voice and speaking contents of the target user at the same time; the preference characteristic analysis is carried out on the target user according to the first characteristic information and the second characteristic information to obtain the pushing preference characteristic, and the accuracy of the user preference analysis can be improved by the preference analysis of the target user; calling an intelligent recommendation model to calculate a target push path corresponding to the push preference characteristics; generating a home building material list to be pushed according to the target pushing path and the pushing preference characteristics; the target household building material recommendation sequence is generated according to the household building material list to be pushed, the target user is recommended according to the target household building material recommendation sequence, and the target user is recommended by inquiring the target household building material recommendation sequence which is most matched with the target user from the large quantity of household building material lists to be pushed, so that the accuracy of intelligent household building material recommendation can be effectively improved.
Drawings
Fig. 1 is a schematic view of an embodiment of an intelligent home building material recommendation method in an embodiment of the invention;
fig. 2 is a schematic view of another embodiment of an intelligent home building material recommendation method in the embodiment of the invention;
fig. 3 is a schematic view of an embodiment of an intelligent home building material recommendation device in an embodiment of the invention;
FIG. 4 is a schematic view of another embodiment of an intelligent home building material recommendation device in accordance with an embodiment of the present invention;
fig. 5 is a schematic view of an embodiment of an intelligent home building material recommendation device in the embodiment of the invention.
Detailed Description
The embodiment of the invention provides an intelligent home building material recommendation method, device, equipment and storage medium, which are used for improving the accuracy of intelligent home building material recommendation. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Moreover, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a specific flow of an embodiment of the present invention is described below, and with reference to fig. 1, an embodiment of a method for recommending an intelligent home building material according to an embodiment of the present invention includes:
101. acquiring dialogue data of a target user based on a preset intelligent question-answering terminal, and performing data extraction on the dialogue data to obtain voice data and text data;
it can be understood that the execution subject of the present invention may be an intelligent home building material recommendation device, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
Specifically, related dialogue data of a target user is acquired from an intelligent question-answering terminal, and it should be noted that in the embodiment of the application, a server extracts conversations among the target users, constructs a multi-round dialogue training set, constructs a deep network model for multi-round dialogue fusion context information and a loss function thereof, takes dialogs and answers in the multi-round dialogue training set as input, trains the deep network model on the training set through the minimum loss function, and inputs the dialogue of the target user into the trained deep network model to extract data, so as to obtain voice data and text data.
102. Performing voice feature extraction on voice data to obtain first feature information of a target user, and generating second feature information of the target user according to text data;
specifically, voice data are determined, the voice data are input into a voice feature extraction model, and first voice features output by the voice feature extraction model are obtained, wherein the first voice feature extraction model is obtained based on sample voice data unsupervised training, the voice extraction model is used for coding the voice data to obtain hidden layer features, and the hidden layer features are subjected to nonlinear space mapping to obtain second voice features. The method provided by the embodiment of the invention saves a large amount of calculation and avoids high-dimensional characteristic loss caused by artificial dimension reduction.
103. Performing preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features of the target user;
specifically, the method comprises the steps of obtaining first characteristic information and second characteristic information, screening a first characteristic data set from the first characteristic information and the second characteristic information by using a correlation analysis method, calculating the first characteristic data set according to a user preference static model to obtain static preference weight, obtaining a user dynamic data set, combining the user dynamic data set with the first characteristic data set to generate second characteristic data, calculating the second characteristic data set according to the user preference dynamic model to obtain dynamic preference weight, establishing a user preference analysis model according to the characteristic data set and the weight, executing user preference analysis to obtain push preference characteristics of a target user, and solving the problem that the user characteristic information is wasted during the user preference analysis.
104. Calling a preset intelligent recommendation model to calculate a target push path corresponding to the push preference characteristic;
specifically, preference characteristics are obtained in a preset intelligent recommendation model, all courses in the intelligent recommendation model are connected in a directed mode through node logic, characteristic information in the preference characteristics and connection information of all the characteristics are obtained according to the preference characteristics and the directed connection, a knowledge graph corresponding to the preference characteristics is generated according to the characteristic information and the connection information, historical preference characteristics of a target object are obtained, pushing weights of all entities in the knowledge graph are determined according to the historical preference characteristics, a target pushing path is determined according to the sequencing of the pushing weights from large to small, and the personalized target pushing path can be generated by the method, so that a path suitable for the target object is obtained.
105. Generating a home building material list to be pushed corresponding to a target user according to the target pushing path and the pushing preference characteristics;
specifically, a historical browsing record of a user is obtained, a target user and a similar user similar to the target user are determined according to the browsing record, a home purchase record of the target user and a home purchase record of the similar user are obtained, a corresponding home list and a home list are respectively extracted from the home purchase record and the home purchase record, and homes not appearing in the home list are pushed to the target user.
106. And generating a target home building material recommendation sequence according to the home building material list to be pushed, and recommending the target user according to the target home building material recommendation sequence.
Specifically, the important concepts and the non-important concepts of the item levels and the interest levels in the user sequences are identified by using the household similarity, then the negative and positive counterfactual user sequences are generated by using the concept importance identification results and a data enhancement means, finally the negative and positive counterfactual user items are contrasted and represented by using a contrast method, actual sequence recommendation is carried out on the obtained sequence recommendation model, and finally the server recommends the target user according to the target household building material recommendation sequence.
In the embodiment of the invention, the dialogue data of a target user is obtained, and the data extraction is carried out on the dialogue data to obtain voice data and text data; voice feature extraction is carried out on voice data to obtain first feature information, second feature information is generated according to text data, and the intention analysis accuracy of a target user is improved by analyzing the voice and speaking contents of the target user at the same time; the preference characteristic analysis is carried out on the target user according to the first characteristic information and the second characteristic information to obtain the pushing preference characteristic, and the accuracy of the user preference analysis can be improved by the preference analysis of the target user; calling an intelligent recommendation model to calculate a target push path corresponding to the push preference characteristics; generating a home building material list to be pushed according to the target pushing path and the pushing preference characteristics; the target home building material recommendation sequence is generated according to the home building material list to be pushed, the target user is recommended according to the target home building material recommendation sequence, and the target user is recommended by inquiring the target home building material recommendation sequence which is most matched with the target user from the large number of home building material lists to be pushed, so that the accuracy of intelligent home building material recommendation can be effectively improved.
Referring to fig. 2, another embodiment of the intelligent home building material recommendation method in the embodiment of the present invention includes:
201. acquiring dialogue data of a target user based on a preset intelligent question-answering terminal, and extracting the dialogue data to obtain voice data and text data;
specifically, in this embodiment, the specific implementation of step 201 is similar to that of step 101, and is not described herein again.
202. Performing voice feature extraction on the voice data to obtain first feature information of a target user, and generating second feature information of the target user according to the text data;
specifically, voice print feature extraction is carried out on voice data to obtain voice print features corresponding to a target user; extracting a plurality of first feature elements from the voiceprint features to obtain a plurality of first feature elements, and generating first feature information of a target user according to the plurality of first feature elements; and extracting a plurality of second characteristic elements from the text data to obtain a plurality of second characteristic elements, and generating second characteristic information of the target user according to the plurality of second characteristic elements.
Optionally, the server collects voice data, establishes a sample database, takes an audio file from the sample database, processes the audio file to obtain an audio frame sequence, and performs fourier transform on each frame in the audio frame sequence to obtain spectrogram information corresponding to the frame; the method comprises the steps of extracting time domain information and frequency domain information to obtain time domain characteristics and frequency domain characteristics, performing characteristic aggregation on the time domain characteristics and the frequency domain characteristics to obtain aggregated first characteristic elements, performing vector embedding on the aggregated characteristic elements to obtain voiceprint characteristic vectors, inputting the voiceprint characteristic vectors to a convolutional neural network model for training to obtain second characteristic elements, generating second characteristic information of a target user according to the plurality of second characteristic elements, and improving accuracy and efficiency of voiceprint characteristic extraction.
203. Performing preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features of the target user;
specifically, a plurality of first feature elements in the first feature information are extracted, and a plurality of second feature elements in the second feature information are extracted; constructing a user feature set based on the plurality of first feature elements and the plurality of second feature elements; and carrying out preference characteristic analysis on the user characteristic set to obtain the pushing preference characteristic of the target user.
The method comprises the steps of obtaining first characteristic information and second characteristic information, screening a first characteristic data set from the first characteristic information and the second characteristic information by using a correlation analysis method, calculating the first characteristic data set according to a user preference static model to obtain static preference weight, obtaining a user dynamic data set, combining the user dynamic data set with the first characteristic data set to generate a second characteristic data set, calculating the second characteristic data set according to the user preference dynamic model to obtain dynamic preference weight, establishing a user preference analysis model according to the characteristic data set and the weight, and executing user preference analysis to obtain push preference characteristics of a target user.
204. Calling a preset intelligent recommendation model to calculate a target push path corresponding to the push preference characteristic;
specifically, a preset intelligent recommendation model is called to perform pushing path configuration on the pushing preference characteristics to obtain a pushing path configuration result; performing result data analysis on the configuration result of the push path, and generating a plurality of candidate push paths corresponding to the target user; carrying out pushing path probability calculation on a plurality of candidate pushing paths to obtain a probability value corresponding to each candidate pushing path; and determining a target push path according to the probability value corresponding to each candidate push path.
Optionally, an identifier of the target user is obtained, a characteristic value corresponding to the identifier of the target user under a preset user attribute type is obtained, data content is obtained, and a decision tree object corresponding to the data content is searched; the method comprises the steps of positioning leaf nodes corresponding to the identification of a target user in a decision tree object according to the characteristic value corresponding to the identification of the target user under the preset user attribute type, obtaining the click number and the push number stored in the positioned leaf nodes, generating a selection reference value according to the click number and the push number, selecting data content according to the selection reference value, and determining a target push path according to the probability value corresponding to each candidate push path, so that the push accuracy can be improved.
205. Generating a home building material list to be pushed corresponding to a target user according to the target pushing path and the pushing preference characteristics;
specifically, a preset database is called to perform clustering processing on the path node data to obtain a clustering result corresponding to the path node data; and establishing a home building material list to be pushed corresponding to the target user according to the clustering result.
The method comprises the steps of obtaining a list of home building materials to be pushed, carrying out standardized preprocessing on the list of the home building materials to be pushed to generate a data set to be clustered, carrying out clustering processing on all data to be clustered in the data set to be clustered to obtain initial class data, carrying out class integration on the initial class data to obtain a clustering result, judging whether the clustering result meets a clustering target requirement, stopping clustering processing if the clustering result meets the clustering target requirement, continuing clustering processing if the clustering result does not meet the clustering target requirement, creating the list of the home building materials to be pushed corresponding to a target user according to the clustering result after the clustering result corresponding to path node data is obtained, effectively adapting to clustering analysis of high-dimensional data, and obtaining a clustering result with continuous and compact space and high calculation efficiency.
206. Generating a target home building material recommendation sequence according to the home building material list to be pushed, and recommending a target user according to the target home building material recommendation sequence;
specifically, a target home building material recommendation sequence is generated according to a home building material list to be pushed; extracting user real-time behavior data corresponding to a target user from a database; according to the real-time behavior data of the user, at least two home building materials to be recommended are selected from the target home building material recommendation sequence, and the at least two home building materials to be recommended are recommended to the target user.
The method comprises the steps of obtaining user real-time behavior data of a target user, dividing the user real-time behavior data into a long-term behavior sequence and a short-term behavior sequence, inputting the long-term behavior sequence and the short-term behavior sequence into a recommendation model for recommendation, and obtaining a target recommendation object, wherein the recommendation model is a multi-layer self-attention network sequence recommendation model integrating long-term and short-term preferences of the user, and the target recommendation object is pushed to the target user, so that recommendation is more accurate, and user experience can be improved.
207. Acquiring feedback data after a target user receives push;
208. and sequentially adjusting the target household building material recommendation sequence according to the feedback data.
Specifically, in response to a feedback data display operation triggered by a target user display interface, jumping to the feedback data display interface, acquiring feedback data of multiple dimensions corresponding to a target user, in response to a data dimension selection operation of the user in a data dimension selection area, determining feedback data of at least one target dimension from the feedback data of the multiple dimensions, generating a target view in a view area based on the feedback data of the at least one target dimension, and sequentially adjusting a target home building material recommendation sequence according to the feedback data.
In the embodiment of the invention, dialogue data of a target user is obtained, and data extraction is carried out on the dialogue data to obtain voice data and text data; voice feature extraction is carried out on voice data to obtain first feature information, second feature information is generated according to text data, and the intention analysis accuracy of a target user is improved by analyzing the voice and speaking contents of the target user at the same time; the preference characteristic analysis is carried out on the target user according to the first characteristic information and the second characteristic information to obtain the pushing preference characteristic, and the accuracy of the user preference analysis can be improved by the preference analysis of the target user; calling an intelligent recommendation model to calculate a target push path corresponding to the push preference feature; generating a home building material list to be pushed according to the target pushing path and the pushing preference characteristics; the target household building material recommendation sequence is generated according to the household building material list to be pushed, the target user is recommended according to the target household building material recommendation sequence, and the target user is recommended by inquiring the target household building material recommendation sequence which is most matched with the target user from the large quantity of household building material lists to be pushed, so that the accuracy of intelligent household building material recommendation can be effectively improved.
With reference to fig. 3, the method for recommending smart home building materials in the embodiment of the present invention is described above, and an embodiment of the device for recommending smart home building materials in the embodiment of the present invention is described below, where:
the acquisition module 301 is configured to acquire dialog data of a target user based on a preset intelligent question-answering terminal, and perform data extraction on the dialog data to obtain voice data and text data;
an extraction module 302, configured to perform voice feature extraction on the voice data to obtain first feature information of the target user, and generate second feature information of the target user according to the text data;
the analysis module 303 is configured to perform preference feature analysis on the target user according to the first feature information and the second feature information to obtain a push preference feature of the target user;
the calculating module 304 is configured to invoke a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature;
a generating module 305, configured to generate a list of home building materials to be pushed corresponding to the target user according to the target pushing path and the pushing preference feature;
and the recommending module 306 is configured to generate a target home building material recommending sequence according to the home building material list to be pushed, and recommend the target user according to the target home building material recommending sequence.
In the embodiment of the invention, the dialogue data of a target user is obtained, and the data extraction is carried out on the dialogue data to obtain voice data and text data; voice feature extraction is carried out on voice data to obtain first feature information, second feature information is generated according to text data, and the intention analysis accuracy of a target user is improved by analyzing the voice and speaking contents of the target user at the same time; the preference characteristic analysis is carried out on the target user according to the first characteristic information and the second characteristic information to obtain the pushing preference characteristic, and the accuracy of the user preference analysis can be improved by the preference analysis of the target user; calling an intelligent recommendation model to calculate a target push path corresponding to the push preference characteristics; generating a home building material list to be pushed according to the target pushing path and the pushing preference characteristics; the target household building material recommendation sequence is generated according to the household building material list to be pushed, the target user is recommended according to the target household building material recommendation sequence, and the target user is recommended by inquiring the target household building material recommendation sequence which is most matched with the target user from the large quantity of household building material lists to be pushed, so that the accuracy of intelligent household building material recommendation can be effectively improved.
Referring to fig. 4, another embodiment of the intelligent home building material recommendation device in the embodiment of the present invention includes:
the acquisition module 301 is configured to acquire dialog data of a target user based on a preset intelligent question and answer terminal, and perform data extraction on the dialog data to obtain voice data and text data;
an extraction module 302, configured to perform voice feature extraction on the voice data to obtain first feature information of the target user, and generate second feature information of the target user according to the text data;
the analysis module 303 is configured to perform preference feature analysis on the target user according to the first feature information and the second feature information to obtain a push preference feature of the target user;
the calculating module 304 is configured to invoke a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature;
a generating module 305, configured to generate a list of home building materials to be pushed, corresponding to the target user, according to the target pushing path and the pushing preference feature;
and the recommending module 306 is configured to generate a target home building material recommending sequence according to the home building material list to be pushed, and recommend the target user according to the target home building material recommending sequence.
Optionally, the extracting module 302 is specifically configured to:
voice print feature extraction is carried out on the voice data, and voice print features corresponding to the target user are obtained; extracting a plurality of first feature elements from the voiceprint features to obtain a plurality of first feature elements, and generating first feature information of the target user according to the plurality of first feature elements; and extracting a plurality of second characteristic elements from the text data to obtain a plurality of second characteristic elements, and generating second characteristic information of the target user according to the plurality of second characteristic elements.
Optionally, the analysis module 303 is specifically configured to:
extracting a plurality of first feature elements in the first feature information and extracting a plurality of second feature elements in the second feature information; constructing a user feature set based on the plurality of first feature elements and the plurality of second feature elements; and carrying out preference characteristic analysis on the user characteristic set to obtain the push preference characteristic of the target user.
Optionally, the calculating module 304 is specifically configured to:
calling a preset intelligent recommendation model to carry out push path configuration on the push preference characteristics to obtain a push path configuration result; performing result data analysis on the configuration result of the push path, and generating a plurality of candidate push paths corresponding to the target user; carrying out pushing path probability calculation on the candidate pushing paths to obtain a probability value corresponding to each candidate pushing path; and determining a target pushing path according to the probability value corresponding to each candidate pushing path.
Optionally, the generating module 305 is specifically configured to:
performing path node analysis on the target pushing path to obtain path node data; calling a preset database to perform clustering processing on the path node data to obtain a clustering result corresponding to the path node data; and creating a home building material list to be pushed corresponding to the target user according to the clustering result.
Optionally, the recommending module 306 is specifically configured to:
generating a target home building material recommendation sequence according to the home building material list to be pushed; extracting user real-time behavior data corresponding to the target user from the database; and selecting at least two home building materials to be recommended from the target home building material recommendation sequence according to the real-time behavior data of the user, and recommending the at least two home building materials to be recommended to the target user.
Optionally, the smart home building material recommending apparatus further includes:
an adjusting module 307, configured to obtain feedback data after the target user receives the push; and sequentially adjusting the target home building material recommendation sequence according to the feedback data.
In the embodiment of the invention, the dialogue data of a target user is obtained, and the data extraction is carried out on the dialogue data to obtain voice data and text data; voice feature extraction is carried out on voice data to obtain first feature information, second feature information is generated according to text data, and the intention analysis accuracy of a target user is improved by analyzing the voice and speaking contents of the target user at the same time; the preference characteristic analysis is carried out on the target user according to the first characteristic information and the second characteristic information to obtain the pushing preference characteristic, and the accuracy of the user preference analysis can be improved by the preference analysis of the target user; calling an intelligent recommendation model to calculate a target push path corresponding to the push preference feature; generating a home building material list to be pushed according to the target pushing path and the pushing preference characteristics; the target home building material recommendation sequence is generated according to the home building material list to be pushed, the target user is recommended according to the target home building material recommendation sequence, and the target user is recommended by inquiring the target home building material recommendation sequence which is most matched with the target user from the large number of home building material lists to be pushed, so that the accuracy of intelligent home building material recommendation can be effectively improved.
Fig. 3 and 4 describe the intelligent home building material recommendation device in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the intelligent home building material recommendation device in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of an intelligent home building material recommendation device according to an embodiment of the present invention, where the intelligent home building material recommendation device 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors), a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient storage or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown), each of which may include a series of instructions operating on the smart home building material recommendation device 500. Further, the processor 510 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the smart home building material recommendation device 500.
The smart home building materials recommendation device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input/output interfaces 560, and/or one or more operating systems 531, such as Windows Server, mac OS X, unix, linux, freeBSD, and the like. Those skilled in the art will appreciate that the structure of the smart home building material recommendation device shown in fig. 5 is not limited to the smart home building material recommendation device, and may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
The invention further provides intelligent home building material recommendation equipment which comprises a memory and a processor, wherein computer readable instructions are stored in the memory, and when the computer readable instructions are executed by the processor, the processor executes the steps of the intelligent home building material recommendation method in each embodiment.
The invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, having instructions stored therein, which when executed on a computer, cause the computer to perform the steps of the smart home building material recommendation method.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a portable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The intelligent household building material recommendation method is characterized by comprising the following steps:
the method comprises the steps that conversation data of a target user are obtained based on a preset intelligent question-answering terminal, and data extraction is carried out on the conversation data to obtain voice data and text data;
performing voice feature extraction on the voice data to obtain first feature information of the target user, and generating second feature information of the target user according to the text data;
performing preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features of the target user;
calling a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature;
generating a home building material list to be pushed corresponding to the target user according to the target pushing path and the pushing preference characteristics;
and generating a target home building material recommendation sequence according to the home building material list to be pushed, and recommending the target user according to the target home building material recommendation sequence.
2. The intelligent home building material recommendation method according to claim 1, wherein the performing voice feature extraction on the voice data to obtain first feature information of the target user, and generating second feature information of the target user according to the text data comprises:
performing voiceprint feature extraction on the voice data to obtain a voiceprint feature corresponding to the target user;
extracting a plurality of first feature elements from the voiceprint features to obtain a plurality of first feature elements, and generating first feature information of the target user according to the plurality of first feature elements;
and extracting a plurality of second characteristic elements from the text data to obtain a plurality of second characteristic elements, and generating second characteristic information of the target user according to the plurality of second characteristic elements.
3. The intelligent home building material recommendation method according to claim 2, wherein the step of performing preference feature analysis on the target user according to the first feature information and the second feature information to obtain a push preference feature of the target user comprises the steps of:
extracting a plurality of first feature elements in the first feature information and extracting a plurality of second feature elements in the second feature information;
constructing a user feature set based on the plurality of first feature elements and the plurality of second feature elements;
and carrying out preference characteristic analysis on the user characteristic set to obtain the push preference characteristic of the target user.
4. The intelligent home building material recommendation method according to claim 1, wherein the invoking a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature comprises:
calling a preset intelligent recommendation model to perform pushing path configuration on the pushing preference characteristics to obtain a pushing path configuration result;
performing result data analysis on the configuration result of the push path, and generating a plurality of candidate push paths corresponding to the target user;
carrying out pushing path probability calculation on the multiple candidate pushing paths to obtain a probability value corresponding to each candidate pushing path;
and determining a target push path according to the probability value corresponding to each candidate push path.
5. The intelligent home building material recommendation method according to claim 1, wherein the generating a list of home building materials to be pushed corresponding to the target user according to the target push path and the push preference feature comprises:
performing path node analysis on the target pushing path to obtain path node data;
calling a preset database to perform clustering processing on the path node data to obtain a clustering result corresponding to the path node data;
and creating a home building material list to be pushed corresponding to the target user according to the clustering result.
6. The intelligent home building material recommendation method according to claim 5, wherein the generating a target home building material recommendation sequence according to the home building material list to be pushed and recommending the target user according to the target home building material recommendation sequence comprises:
generating a target home building material recommendation sequence according to the home building material list to be pushed;
extracting user real-time behavior data corresponding to the target user from the database;
and selecting at least two home building materials to be recommended from the target home building material recommendation sequence according to the real-time behavior data of the user, and recommending the at least two home building materials to be recommended to the target user.
7. The intelligent home building material recommendation method according to any one of claims 1-6, further comprising:
acquiring feedback data of the target user after receiving the push;
and sequentially adjusting the target home building material recommendation sequence according to the feedback data.
8. The utility model provides an intelligence house building materials recommendation device which characterized in that, intelligence house building materials recommendation device includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring dialogue data of a target user based on a preset intelligent question-answering terminal and extracting the data of the dialogue data to obtain voice data and text data;
the extraction module is used for extracting voice characteristics of the voice data to obtain first characteristic information of the target user and generating second characteristic information of the target user according to the text data;
the analysis module is used for carrying out preference feature analysis on the target user according to the first feature information and the second feature information to obtain push preference features of the target user;
the calculation module is used for calling a preset intelligent recommendation model to calculate a target push path corresponding to the push preference feature;
the generation module is used for generating a home building material list to be pushed corresponding to the target user according to the target pushing path and the pushing preference characteristics;
and the recommending module is used for generating a target home building material recommending sequence according to the home building material list to be pushed and recommending the target user according to the target home building material recommending sequence.
9. The utility model provides an intelligence house building materials recommendation equipment which characterized in that, intelligence house building materials recommendation equipment includes: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the smart home building material recommendation device to perform the smart home building material recommendation method according to any one of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, implement the smart home building materials recommendation method according to any one of claims 1-7.
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