CN117349531A - User information recommendation method and system based on smart home - Google Patents
User information recommendation method and system based on smart home Download PDFInfo
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Abstract
The invention provides a user information recommendation method and system based on smart home, and relates to the technical field of data processing. In the invention, based on corresponding intelligent home equipment operation data, a plurality of intelligent home users are subjected to user matching processing so as to determine a user matching relationship among the plurality of intelligent home users; determining information recommended smart home users to be processed among a plurality of smart home users; determining each matching intelligent home user corresponding to the intelligent home user to be processed from a plurality of intelligent home users based on the user matching relationship; based on the user behavior characteristic information corresponding to the smart home user to be processed and the user behavior characteristic information corresponding to each matched smart home user, user information recommendation operation is carried out on the smart home user to be processed, and target recommendation data of the smart home user to be processed are determined. Based on the method, the reliability of user information recommendation can be improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a user information recommendation method and system based on smart home.
Background
With the continuous development of computer technology and internet technology, the application scenes of the system are increased, for example, applications in the field of home have led to the advent of smart homes, which are becoming increasingly mature. In the smart home application, more data are generated, and the data have more application value. For example, the method can be used for matching users, and for example, user information recommendation can also be performed, but the reliability is poor in the process of user information recommendation.
Disclosure of Invention
Therefore, the invention aims to provide a user information recommendation method and system based on smart home so as to improve the reliability of user information recommendation.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
a user information recommendation method based on smart home comprises the following steps:
based on corresponding intelligent home equipment operation data, performing user matching processing on a plurality of intelligent home users to determine a user matching relationship among the plurality of intelligent home users;
determining information recommended smart home users to be processed among the plurality of smart home users;
Determining each matching intelligent home user corresponding to the intelligent home user to be processed from the plurality of intelligent home users based on the user matching relationship;
based on the user behavior characteristic information corresponding to the smart home user to be processed and the user behavior characteristic information corresponding to each matched smart home user, performing user information recommendation operation on the smart home user to be processed, determining target recommendation data of the smart home user to be processed, wherein the user behavior characteristic information is used for reflecting the user behaviors of the corresponding smart home users.
In some preferred embodiments, in the smart home based user information recommendation method, the step of determining a smart home user to be processed for information recommendation among the plurality of smart home users includes:
for each intelligent home user in the plurality of intelligent home users, determining each matched intelligent home user corresponding to the intelligent home user based on the user matching relationship, and counting the number of the matched intelligent home users corresponding to the intelligent home user;
determining the corresponding intelligent home user as a candidate intelligent home user under the condition that the number of the corresponding matching intelligent home users is larger than or equal to the preset reference number;
And selecting one candidate intelligent home user from each determined candidate intelligent home user to mark the candidate intelligent home users as information recommendation to be processed.
In some preferred embodiments, in the above method for recommending user information based on smart home, the step of determining target recommendation data of the smart home user to be processed based on user behavior feature information corresponding to the smart home user to be processed and user behavior feature information corresponding to each of the matching smart home users includes:
acquiring user behavior characteristic information corresponding to the smart home user to be processed to obtain first user behavior characteristic information, and acquiring user behavior characteristic information corresponding to each matched smart home user to obtain second user behavior characteristic information;
performing key information mining operation on the first user behavior feature information to form corresponding first user behavior characterization vectors, and performing key information mining operation on each piece of second user behavior feature information to form corresponding second user behavior characterization vectors;
And based on the first user behavior characterization vector and the second user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
In some preferred embodiments, in the smart home based user information recommendation method, the step of performing a user information recommendation operation on the smart home user to be processed based on the first user behavior characterization vector and the second user behavior characterization vector to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data includes:
for each second user behavior characterization vector, performing reinforcement operation on the first user behavior characterization vector based on the second user behavior characterization vector to form one reinforced user behavior characterization vector corresponding to the first user behavior characterization vector;
and based on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
In some preferred embodiments, in the smart home-based user information recommendation method, the step of performing, for each of the second user behavior characterization vectors, an enhancement operation on the first user behavior characterization vector based on the second user behavior characterization vector to form an enhanced user behavior characterization vector corresponding to the first user behavior characterization vector includes:
performing transposition operation on the second user behavior representation vector to form a transposed second user behavior representation vector corresponding to the second user behavior representation vector, and determining the vector dimension of the first user behavior representation vector to obtain a first dimension number;
multiplying the transposed second user behavior characterization vector and the first user behavior characterization vector to output a corresponding correlation characterization vector, and dividing the correlation characterization vector by the first number of dimensions to form a corresponding adjusted correlation characterization vector;
and carrying out mapping processing of vector parameters on the adjustment correlation characterization vector to form a mapping characterization vector corresponding to the adjustment correlation characterization vector, and multiplying the mapping characterization vector and the second user behavior characterization vector to output an enhanced user behavior characterization vector corresponding to the first user behavior characterization vector.
In some preferred embodiments, in the smart home based user information recommendation method, the step of performing a user information recommendation operation on the smart home user to be processed based on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data includes:
performing cascade combination processing on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector to form cascade combination behavior characterization vectors;
performing linear integration operation on the cascade combined behavior characterization vector to output a linear integration characterization vector corresponding to the smart home user to be processed, and performing vector parameter compression operation on the linear integration characterization vector to form a corresponding compressed linear integration characterization vector;
for each candidate recommendation data in the plurality of candidate recommendation data, carrying out key information mining operation on the candidate recommendation data, outputting a recommendation characterization vector corresponding to the candidate recommendation data, and determining a vector matching parameter between the recommendation characterization vector and the compressed linear integration characterization vector;
And determining a target vector matching parameter in vector matching parameters between a recommendation characteristic vector corresponding to each candidate recommendation data and the compressed linear integration characteristic vector, and taking the candidate recommendation data corresponding to the target vector matching parameter as target recommendation data of the smart home user to be processed, wherein the candidate recommendation data is text data or image data.
In some preferred embodiments, in the method for recommending user information based on smart home, the step of performing user matching processing on a plurality of smart home users based on the corresponding smart home device operation data to determine a user matching relationship among the plurality of smart home users includes:
extracting a plurality of intelligent home equipment operation data corresponding to a first intelligent home user, wherein the first intelligent home user and a second intelligent home user have a matching relationship, and the intelligent home equipment operation data is at least used for reflecting the use condition of the first intelligent home user on corresponding intelligent home equipment;
performing data decomposition operation on the plurality of intelligent home equipment operation data to form a plurality of local equipment operation data corresponding to each intelligent home equipment operation data, and performing screening operation on the plurality of local equipment operation data corresponding to each intelligent home equipment operation data to form a target equipment operation behavior corresponding to each local equipment operation data, wherein each local equipment operation data comprises a plurality of equipment operation behaviors;
Performing key information mining operation on the target equipment operation behaviors corresponding to each piece of local equipment operation data respectively to output behavior key information representation vectors corresponding to each piece of local equipment operation data, and analyzing a first equipment operation representation vector corresponding to the first smart home user according to the behavior key information representation vectors;
performing reinforcement operation on a first equipment operation representation vector corresponding to the first smart home user to form a second equipment operation representation vector corresponding to the first smart home user;
according to the second equipment operation characterization vector corresponding to the first smart home user, a matched third smart home user is screened out, and user matching operation on the second smart home user is conducted on the third smart home user, so that the first smart home user, the third smart home user and the second smart home user have a matching relationship with each other.
The embodiment of the invention also provides a user information recommendation system based on smart home, which comprises the following steps:
the user matching processing module is used for carrying out user matching processing on a plurality of intelligent home users based on corresponding intelligent home equipment operation data so as to determine a user matching relationship among the plurality of intelligent home users;
The recommendation user determining module is used for determining information recommendation to-be-processed intelligent home users in the plurality of intelligent home users;
the matching user determining module is used for determining each matching intelligent home user corresponding to the intelligent home user to be processed in the plurality of intelligent home users based on the user matching relationship;
the user information recommending module is used for recommending the user information to the intelligent home users to be processed based on the user behavior characteristic information corresponding to the intelligent home users to be processed and the user behavior characteristic information corresponding to each matched intelligent home user, and determining target recommending data of the intelligent home users to be processed, wherein the user behavior characteristic information is used for reflecting the user behaviors of the corresponding intelligent home users.
In some preferred embodiments, in the smart home-based user information recommendation system, the recommendation user determining module is specifically configured to:
for each intelligent home user in the plurality of intelligent home users, determining each matched intelligent home user corresponding to the intelligent home user based on the user matching relationship, and counting the number of the matched intelligent home users corresponding to the intelligent home user;
Determining the corresponding intelligent home user as a candidate intelligent home user under the condition that the number of the corresponding matching intelligent home users is larger than or equal to the preset reference number;
and selecting one candidate intelligent home user from each determined candidate intelligent home user to mark the candidate intelligent home users as information recommendation to be processed.
In some preferred embodiments, in the smart home-based user information recommendation system, the user information recommendation module is specifically configured to:
acquiring user behavior characteristic information corresponding to the smart home user to be processed to obtain first user behavior characteristic information, and acquiring user behavior characteristic information corresponding to each matched smart home user to obtain second user behavior characteristic information;
performing key information mining operation on the first user behavior feature information to form corresponding first user behavior characterization vectors, and performing key information mining operation on each piece of second user behavior feature information to form corresponding second user behavior characterization vectors;
and based on the first user behavior characterization vector and the second user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
The user information recommendation method and system based on the smart home provided by the embodiment of the invention can be used for carrying out user matching processing on a plurality of smart home users based on corresponding smart home equipment operation data so as to determine a user matching relationship among the plurality of smart home users; determining information recommended smart home users to be processed among a plurality of smart home users; determining each matching intelligent home user corresponding to the intelligent home user to be processed from a plurality of intelligent home users based on the user matching relationship; based on the user behavior characteristic information corresponding to the smart home user to be processed and the user behavior characteristic information corresponding to each matched smart home user, user information recommendation operation is carried out on the smart home user to be processed, and target recommendation data of the smart home user to be processed are determined. Based on the above, in the user information recommendation operation, not only the user behavior characteristic information corresponding to the smart home user to be processed is taken as a basis, but also the user behavior characteristic information corresponding to the corresponding matching smart home user is taken as a basis, so that the basis can be more sufficient, the reliability of the user information recommendation can be improved, and the problem of poor reliability in the prior art is solved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a user information recommendation platform based on smart home according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating steps involved in a smart home-based user information recommendation method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of each module included in the smart home-based user information recommendation system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention provides a user information recommendation platform based on smart home. Wherein the user information recommendation platform may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the smart home-based user information recommendation method provided by the embodiment of the present invention (as described below).
Alternatively, in some embodiments, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
Alternatively, in some embodiments, the processor may be a general purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Alternatively, in some embodiments, the smart home based user information recommendation platform may be a server with data processing capabilities.
With reference to fig. 2, the embodiment of the invention further provides a user information recommendation method based on the smart home, which can be applied to the user information recommendation platform based on the smart home. The method steps defined by the flow related to the intelligent home based user information recommendation method can be realized by the intelligent home based user information recommendation platform.
The specific flow shown in fig. 2 will be described in detail.
Step S100, based on corresponding intelligent home equipment operation data, performing user matching processing on a plurality of intelligent home users so as to determine a user matching relationship among the plurality of intelligent home users.
In the embodiment of the invention, the intelligent home based user information recommendation platform can perform user matching processing on a plurality of intelligent home users based on corresponding intelligent home equipment operation data so as to determine a user matching relationship among the plurality of intelligent home users.
Step S200, determining the information recommended smart home users to be processed among the plurality of smart home users.
In the embodiment of the invention, the intelligent home based user information recommendation platform can determine the information recommended to-be-processed intelligent home user from the plurality of intelligent home users.
And step S300, determining each matching smart home user corresponding to the smart home user to be processed from the plurality of smart home users based on the user matching relationship.
In the embodiment of the invention, the intelligent home based user information recommendation platform can determine each matching intelligent home user corresponding to the to-be-processed intelligent home user from the plurality of intelligent home users based on the user matching relationship.
Step S400, based on the user behavior feature information corresponding to the smart home users to be processed and the user behavior feature information corresponding to each matched smart home user, performing user information recommendation operation on the smart home users to be processed, and determining target recommendation data of the smart home users to be processed.
In the embodiment of the invention, the intelligent home based user information recommendation platform can perform user information recommendation operation on the intelligent home user to be processed based on the user behavior characteristic information corresponding to the intelligent home user to be processed and the user behavior characteristic information corresponding to each matched intelligent home user, and determine target recommendation data of the intelligent home user to be processed. The user behavior characteristic information is used for reflecting the user behaviors of the corresponding smart home users.
Based on the above, in the user information recommendation operation, not only the user behavior characteristic information corresponding to the smart home user to be processed is taken as a basis, but also the user behavior characteristic information corresponding to the corresponding matching smart home user is taken as a basis, so that the basis can be more sufficient, the reliability of the user information recommendation can be improved, and the problem of poor reliability in the prior art is solved.
Optionally, in some embodiments, the user matching process is performed on a plurality of smart home users based on the corresponding smart home device operation data, so as to determine a user matching relationship among the plurality of smart home users, which may further include the following content, such as step S110, step S120, step S130, step S140, and step S150.
Step S110, a plurality of intelligent home equipment operation data corresponding to the first intelligent home user are extracted.
In the embodiment of the invention, the user information recommendation platform based on the smart home can extract a plurality of pieces of smart home equipment operation data corresponding to the first smart home user. The intelligent home equipment operation data are at least used for reflecting the service condition of the first intelligent home user on corresponding intelligent home equipment. The smart home device operation data may exist in the form of user text.
Step S120, performing a data decomposition operation on the plurality of smart home device operation data to form a plurality of local device operation data corresponding to each of the smart home device operation data, and performing a screening operation on the plurality of local device operation data corresponding to each of the smart home device operation data to form a target device operation behavior corresponding to each of the local device operation data.
In the embodiment of the invention, the smart home based user information recommendation platform may perform data decomposition operation on the plurality of smart home device operation data to form a plurality of local device operation data corresponding to each of the smart home device operation data, and perform screening operation on the plurality of local device operation data corresponding to each of the smart home device operation data to form a target device operation behavior corresponding to each of the local device operation data. Each of the local device operation data includes a plurality of device operation behaviors, such as a first time start, a second time perform a first function operation, a third time perform a first function operation, and a fourth time shut down.
Step S130, performing a key information mining operation on the target device operation behavior corresponding to each piece of local device operation data, so as to output a behavior key information characterization vector corresponding to each piece of local device operation data, and analyzing a first device operation characterization vector corresponding to the first smart home user according to the behavior key information characterization vector.
In the embodiment of the invention, the smart home-based user information recommendation platform can perform key information mining operation on the target device operation behaviors corresponding to each piece of local device operation data respectively so as to output the behavior key information representation vector corresponding to each piece of local device operation data, and analyze the first device operation representation vector corresponding to the first smart home user according to the behavior key information representation vector.
Step S140, performing reinforcement operation on the first device operation representation vector corresponding to the first smart home user to form a second device operation representation vector corresponding to the first smart home user.
In the embodiment of the invention, the smart home based user information recommendation platform can strengthen the first equipment operation characterization vector corresponding to the first smart home user, namely strengthen the first equipment operation characterization vector to form the second equipment operation characterization vector corresponding to the first smart home user.
Step S150, screening out a matched third smart home user according to the second device operation characterization vector corresponding to the first smart home user, and performing a user matching operation on the second smart home user on the third smart home user.
In the embodiment of the invention, the user information recommendation platform based on the smart home can screen out a matched third smart home user according to the second equipment operation characterization vector corresponding to the first smart home user, and perform user matching operation on the second smart home user on the third smart home user.
Based on this, as in the above steps S110-S150, since the mined first device operation characterization vector is subjected to the strengthening operation before the matching of the smart home user is performed, the matching of the smart home user can be performed based on the obtained second device operation characterization vector, that is, the basis of the matching of the smart home user can be more reliable, so that the reliability of the matching of the user can be improved, thereby improving the problem of poor reliability in the prior art.
Optionally, in some embodiments, the step of performing a key information mining operation on the target device operation behaviors corresponding to each piece of local device operation data to output a behavior key information representation vector corresponding to each piece of local device operation data, and analyzing, according to the behavior key information representation vector, a first device operation representation vector corresponding to the first smart home user may further include the following contents:
Performing key information mining operation on the target device operation behaviors corresponding to each piece of local device operation data respectively, for example, mapping the target device operation behaviors to a feature space to output a behavior key information representation vector corresponding to each piece of local device operation data;
and performing cascading combination operation on the behavior key information characterization vector corresponding to each piece of local equipment operation data to form a first equipment operation characterization vector corresponding to the first smart home user.
Optionally, in some embodiments, the step of performing a cascade combination operation on the behavior key information token vector corresponding to each piece of local device operation data to form a first device operation token vector corresponding to the first smart home user may include the following contents:
performing cascading combination operation on the behavior key information characterization vector corresponding to each piece of local equipment operation data to output a corresponding cascading combination characterization vector, such as a behavior key information characterization vector 1-behavior key information characterization vector 2-behavior key information characterization vector 3;
and performing linear integration operation on the cascade combination characterization vector to output a first equipment operation characterization vector corresponding to the first smart home user, wherein the linear integration operation can be realized based on an MLP (multi-level processor), namely MultiLayer Perceptron.
Optionally, in some embodiments, the step of performing the strengthening operation on the first device operation token vector corresponding to the first smart home user to form the second device operation token vector corresponding to the first smart home user may further include the following contents:
utilizing an a-th focusing characteristic analysis unit in the A-th sequentially connected focusing characteristic analysis units to perform focusing characteristic analysis operation on data to be processed of the a-th focusing characteristic analysis unit so as to output an a-th focusing characteristic analysis characterization vector;
loading the a-th focusing characteristic analysis characterization vector into a b-th focusing characteristic analysis unit, and performing focusing characteristic analysis operation by using the b-th focusing characteristic analysis unit to form a b-th focusing characteristic analysis characterization vector, wherein the b-th focusing characteristic analysis unit is a focusing characteristic analysis unit behind the a-th focusing characteristic analysis unit;
it can be understood that the data to be processed of the 1 st focusing characteristic analysis unit is the first device operation characterization vector, the data to be processed of each focusing characteristic analysis unit except the 1 st focusing characteristic analysis unit is the focusing characteristic analysis characterization vector output by the previous focusing characteristic analysis unit, and the focusing characteristic analysis characterization vector output by the last focusing characteristic analysis unit is the second device operation characterization vector corresponding to the first smart home user.
Optionally, in some embodiments, the step of performing the focus feature analysis operation on the data to be processed of the a-th focus feature analysis unit by using the a-th focus feature analysis unit in the a-th sequentially connected focus feature analysis units to output the a-th focus feature analysis characterization vector may further include the following steps:
the method comprises the steps that a, a first focusing characteristic analysis unit is utilized, data to be processed of the first focusing characteristic analysis unit are subjected to vector parameter compression operation, so that compression characteristic vectors corresponding to first equipment operation characteristic vectors of a first smart home user are output, the vector parameter compression operation can be used for sliding a window on the basis of a preset window, then, average value or maximum value of vector parameters in the window are determined to serve as representative of each vector parameter in the window, namely, each vector parameter in the window is replaced by one representative, and compression is achieved;
multiplying a compressed representation vector corresponding to a first equipment operation representation vector of the first smart home user by a transpose result of the compressed representation vector by utilizing the a-th focusing characteristic analysis unit so as to output corresponding importance parameter distribution;
And multiplying the importance parameter distribution corresponding to the first equipment operation characterization vector of the first smart home user and the compression characterization vector corresponding to the first equipment operation characterization vector of the first smart home user to output an a-th focusing feature analysis characterization vector.
Optionally, in some embodiments, the step of screening out a matched third smart home user according to the second device operation characterization vector corresponding to the first smart home user, and performing a user matching operation on the third smart home user with respect to the second smart home user may further include the following contents:
for each smart home user to be matched: extracting intelligent home equipment operation data corresponding to the intelligent home users to be matched; performing key information mining operation on intelligent home equipment operation data corresponding to the intelligent home users to be matched so as to output first equipment operation characterization vectors corresponding to the intelligent home users to be matched; performing reinforcement operation on the first equipment operation characterization vector corresponding to the intelligent home user to be matched, and outputting a second equipment operation characterization vector of the intelligent home user to be matched;
Analyzing vector matching coefficients, such as cosine similarity, between the second equipment operation characterization vector of the first intelligent home user and the second equipment operation characterization vector of each intelligent home user to be matched, and marking the intelligent home users to be matched, of which the vector matching coefficients exceed a predetermined reference vector matching coefficient, so as to be marked as third intelligent home users;
and performing user matching operation on the third smart home user with respect to the second smart home user, so that the second smart home user and the third smart home user serve as smart home users matched with each other.
Optionally, in some embodiments, the second device operation characterization vector corresponding to the first smart home user is formed based on device operation data analysis network processing, based on which the smart home-based user information recommendation method may further include the following contents:
determining a plurality of typical smart home users, and determining a plurality of typical data combinations according to the number of the matched users of the plurality of typical smart home users;
based on the representative second device operation characterization vector corresponding to each representative smart home user in the representative data combination, performing network optimization processing on the initial device operation data analysis network, wherein, for example, in the process of network optimization processing, vector matching parameters between representative second device operation characterization vectors corresponding to the representative smart home users matched in the representative data combination can be pulled, and vector matching parameters between representative second device operation characterization vectors corresponding to the representative smart home users matched in the representative data combination can be pulled.
Optionally, in some embodiments, the step of determining a plurality of typical smart home users and determining a plurality of typical data combinations according to the number of matching users of a plurality of the typical smart home users may further include the following steps:
determining a plurality of typical smart home users;
according to the number of the matched users of the plurality of typical smart home users, analyzing the matched parameters between every two typical smart home users in the plurality of typical smart home users;
screening a plurality of typical data combinations to be confirmed from a plurality of typical smart home users, wherein each typical data combination to be confirmed comprises three typical smart home users, and each two typical data combinations to be confirmed comprise incomplete consistency of the typical smart home users;
among the plurality of typical data combinations to be confirmed, a typical data combination is determined, wherein the typical data combination has a matching parameter between two typical smart home users greater than a pre-configured reference matching parameter (such as the matching, that is, a vector matching parameter between typical second device operation characterization vectors corresponding to the two typical smart home users can be zoomed in, and the typical data combination also has a matching parameter between two typical smart home users less than the reference matching parameter (such as the mismatch, that is, a vector matching parameter between typical second device operation characterization vectors corresponding to the two typical smart home users can be zoomed out).
Optionally, in some embodiments, the step of analyzing the matching parameters between each two typical smart home users in the plurality of typical smart home users according to the number of matching users in the plurality of typical smart home users may further include the following steps:
for every two typical smart home users:
determining the number of matched users corresponding to each typical smart home user in the two typical smart home users;
determining the number of commonly matched users between the two typical smart home users;
and analyzing parameters which have positive correlation with the common matched user quantity and negative correlation with the matched user quantity corresponding to each typical intelligent home user as the matched parameters between the two typical intelligent home users.
Optionally, in some embodiments, the step of determining the information recommended smart home user to be processed from the plurality of smart home users may include the following contents:
for each intelligent home user in the plurality of intelligent home users, determining each matched intelligent home user corresponding to the intelligent home user based on the user matching relationship, and counting the number of the matched intelligent home users corresponding to the intelligent home user;
Under the condition that the number of the corresponding matched intelligent home users is larger than or equal to the preset reference number, the corresponding intelligent home users are determined to be candidate intelligent home users, and the specific numerical values of the reference number are not limited, such as 2, 3, 4, 5, 6 and the like;
and selecting one candidate intelligent home user (optionally) from each determined candidate intelligent home user to mark the candidate intelligent home users as information recommended to be processed.
Optionally, in some embodiments, the step of performing a user information recommendation operation on the smart home user to be processed based on the user behavior feature information corresponding to the smart home user to be processed and the user behavior feature information corresponding to each of the matching smart home users, and determining target recommendation data of the smart home user to be processed may include the following contents:
acquiring user behavior characteristic information corresponding to the smart home user to be processed to obtain first user behavior characteristic information, and acquiring user behavior characteristic information corresponding to each matched smart home user to obtain second user behavior characteristic information;
Performing key information mining operation on the first user behavior feature information to form corresponding first user behavior characterization vectors, and performing key information mining operation on each piece of second user behavior feature information to form corresponding second user behavior characterization vectors;
and based on the first user behavior characterization vector and the second user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
Optionally, in some embodiments, the step of performing a user information recommendation operation on the smart home user to be processed based on the first user behavior characterization vector and the second user behavior characterization vector to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data may further include the following contents:
for each second user behavior characterization vector, performing reinforcement operation on the first user behavior characterization vector based on the second user behavior characterization vector to form one reinforced user behavior characterization vector corresponding to the first user behavior characterization vector;
And based on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
Optionally, in some embodiments, the step of performing, for each of the second user behavior characterization vectors, an enhancement operation on the first user behavior characterization vector based on the second user behavior characterization vector to form an enhanced user behavior characterization vector corresponding to the first user behavior characterization vector may further include the following contents:
performing transposition operation on the second user behavior representation vector to form a transposed second user behavior representation vector corresponding to the second user behavior representation vector, and determining the vector dimension of the first user behavior representation vector to obtain a first dimension number;
multiplying the transposed second user behavior characterization vector and the first user behavior characterization vector to output a corresponding correlation characterization vector, and dividing the correlation characterization vector by the first number of dimensions to form a corresponding adjusted correlation characterization vector;
And carrying out mapping processing (such as mapping to interval 0-1) of vector parameters on the adjustment correlation characterization vector to form a mapping characterization vector corresponding to the adjustment correlation characterization vector, and carrying out multiplication processing on the mapping characterization vector and the second user behavior characterization vector row to output an enhanced user behavior characterization vector corresponding to the first user behavior characterization vector.
Optionally, in some embodiments, the step of performing a user information recommendation operation on the smart home user to be processed based on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data may further include the following contents:
performing cascade combination processing on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector to form cascade combination behavior characterization vectors;
performing linear integration operation on the cascade combined behavior characterization vector to output a linear integration characterization vector corresponding to the smart home user to be processed, and performing vector parameter compression operation on the linear integration characterization vector to form a corresponding compressed linear integration characterization vector;
For each candidate recommendation data in the plurality of candidate recommendation data, carrying out key information mining operation on the candidate recommendation data, outputting a recommendation characterization vector corresponding to the candidate recommendation data, and determining a vector matching parameter between the recommendation characterization vector and the compressed linear integration characterization vector;
and determining a target vector matching parameter (such as a largest vector matching parameter) in vector matching parameters between a recommendation characteristic vector corresponding to each candidate recommendation data and the compressed linear integration characteristic vector, and taking the candidate recommendation data corresponding to the target vector matching parameter as target recommendation data (such as pushing to corresponding user terminal equipment) of the smart home user to be processed, wherein the candidate recommendation data is text data or image data.
With reference to fig. 3, the embodiment of the invention further provides a user information recommendation system based on smart home, which can be applied to the user information recommendation platform based on smart home. The smart home-based user information recommendation system may include:
the user matching processing module is used for carrying out user matching processing on a plurality of intelligent home users based on corresponding intelligent home equipment operation data so as to determine a user matching relationship among the plurality of intelligent home users;
The recommendation user determining module is used for determining information recommendation to-be-processed intelligent home users in the plurality of intelligent home users;
the matching user determining module is used for determining each matching intelligent home user corresponding to the intelligent home user to be processed in the plurality of intelligent home users based on the user matching relationship;
the user information recommending module is used for recommending the user information to the intelligent home users to be processed based on the user behavior characteristic information corresponding to the intelligent home users to be processed and the user behavior characteristic information corresponding to each matched intelligent home user, and determining target recommending data of the intelligent home users to be processed, wherein the user behavior characteristic information is used for reflecting the user behaviors of the corresponding intelligent home users.
Optionally, in some embodiments, the recommendation user determining module is specifically configured to:
for each intelligent home user in the plurality of intelligent home users, determining each matched intelligent home user corresponding to the intelligent home user based on the user matching relationship, and counting the number of the matched intelligent home users corresponding to the intelligent home user;
Determining the corresponding intelligent home user as a candidate intelligent home user under the condition that the number of the corresponding matching intelligent home users is larger than or equal to the preset reference number;
and selecting one candidate intelligent home user from each determined candidate intelligent home user to mark the candidate intelligent home users as information recommendation for the intelligent home to be processed.
Optionally, in some embodiments, the user information recommendation module is specifically configured to:
acquiring user behavior characteristic information corresponding to the smart home user to be processed to obtain first user behavior characteristic information, and acquiring user behavior characteristic information corresponding to each matched smart home user to obtain second user behavior characteristic information;
performing key information mining operation on the first user behavior feature information to form corresponding first user behavior characterization vectors, and performing key information mining operation on each piece of second user behavior feature information to form corresponding second user behavior characterization vectors;
and based on the first user behavior characterization vector and the second user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
In summary, the user information recommendation method and system based on the smart home provided by the invention can perform user matching processing on a plurality of smart home users based on corresponding smart home equipment operation data so as to determine a user matching relationship among the plurality of smart home users; determining information recommended smart home users to be processed among a plurality of smart home users; determining each matching intelligent home user corresponding to the intelligent home user to be processed from a plurality of intelligent home users based on the user matching relationship; based on the user behavior characteristic information corresponding to the smart home user to be processed and the user behavior characteristic information corresponding to each matched smart home user, user information recommendation operation is carried out on the smart home user to be processed, and target recommendation data of the smart home user to be processed are determined. Based on the above, in the user information recommendation operation, not only the user behavior characteristic information corresponding to the smart home user to be processed is taken as a basis, but also the user behavior characteristic information corresponding to the corresponding matching smart home user is taken as a basis, so that the basis can be more sufficient, the reliability of the user information recommendation can be improved, and the problem of poor reliability in the prior art is solved.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The intelligent home based user information recommendation method is characterized by comprising the following steps of:
based on corresponding intelligent home equipment operation data, performing user matching processing on a plurality of intelligent home users to determine a user matching relationship among the plurality of intelligent home users;
determining information recommended smart home users to be processed among the plurality of smart home users;
determining each matching intelligent home user corresponding to the intelligent home user to be processed from the plurality of intelligent home users based on the user matching relationship;
based on the user behavior characteristic information corresponding to the smart home user to be processed and the user behavior characteristic information corresponding to each matched smart home user, performing user information recommendation operation on the smart home user to be processed, determining target recommendation data of the smart home user to be processed, wherein the user behavior characteristic information is used for reflecting the user behaviors of the corresponding smart home users.
2. The smart home based user information recommendation method as claimed in claim 1, wherein the step of determining a smart home user to be processed for information recommendation among the plurality of smart home users comprises:
for each intelligent home user in the plurality of intelligent home users, determining each matched intelligent home user corresponding to the intelligent home user based on the user matching relationship, and counting the number of the matched intelligent home users corresponding to the intelligent home user;
determining the corresponding intelligent home user as a candidate intelligent home user under the condition that the number of the corresponding matching intelligent home users is larger than or equal to the preset reference number;
and selecting one candidate intelligent home user from each determined candidate intelligent home user to mark the candidate intelligent home users as information recommendation to be processed.
3. The smart home based user information recommendation method as claimed in claim 1, wherein the step of determining target recommendation data of the smart home user to be processed based on user behavior feature information corresponding to the smart home user to be processed and user behavior feature information corresponding to each of the matching smart home users performs user information recommendation operation on the smart home user to be processed, includes:
Acquiring user behavior characteristic information corresponding to the smart home user to be processed to obtain first user behavior characteristic information, and acquiring user behavior characteristic information corresponding to each matched smart home user to obtain second user behavior characteristic information;
performing key information mining operation on the first user behavior feature information to form corresponding first user behavior characterization vectors, and performing key information mining operation on each piece of second user behavior feature information to form corresponding second user behavior characterization vectors;
and based on the first user behavior characterization vector and the second user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
4. The smart home-based user information recommendation method as claimed in claim 3, wherein the step of performing a user information recommendation operation on the smart home user to be processed based on the first user behavior characterization vector and the second user behavior characterization vector to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data comprises:
For each second user behavior characterization vector, performing reinforcement operation on the first user behavior characterization vector based on the second user behavior characterization vector to form one reinforced user behavior characterization vector corresponding to the first user behavior characterization vector;
and based on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
5. The smart home-based user information recommendation method of claim 4, wherein for each of the second user behavior characterization vectors, the step of performing an enhancement operation on the first user behavior characterization vector based on the second user behavior characterization vector to form an enhanced user behavior characterization vector corresponding to the first user behavior characterization vector comprises:
performing transposition operation on the second user behavior representation vector to form a transposed second user behavior representation vector corresponding to the second user behavior representation vector, and determining the vector dimension of the first user behavior representation vector to obtain a first dimension number;
Multiplying the transposed second user behavior characterization vector and the first user behavior characterization vector to output a corresponding correlation characterization vector, and dividing the correlation characterization vector by the first number of dimensions to form a corresponding adjusted correlation characterization vector;
and carrying out mapping processing of vector parameters on the adjustment correlation characterization vector to form a mapping characterization vector corresponding to the adjustment correlation characterization vector, and multiplying the mapping characterization vector and the second user behavior characterization vector to output an enhanced user behavior characterization vector corresponding to the first user behavior characterization vector.
6. The smart home-based user information recommendation method of claim 4, wherein the step of performing a user information recommendation operation on the smart home user to be processed based on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data comprises:
performing cascade combination processing on each enhanced user behavior characterization vector corresponding to the first user behavior characterization vector to form cascade combination behavior characterization vectors;
Performing linear integration operation on the cascade combined behavior characterization vector to output a linear integration characterization vector corresponding to the smart home user to be processed, and performing vector parameter compression operation on the linear integration characterization vector to form a corresponding compressed linear integration characterization vector;
for each candidate recommendation data in the plurality of candidate recommendation data, carrying out key information mining operation on the candidate recommendation data, outputting a recommendation characterization vector corresponding to the candidate recommendation data, and determining a vector matching parameter between the recommendation characterization vector and the compressed linear integration characterization vector;
and determining a target vector matching parameter in vector matching parameters between a recommendation characteristic vector corresponding to each candidate recommendation data and the compressed linear integration characteristic vector, and taking the candidate recommendation data corresponding to the target vector matching parameter as target recommendation data of the smart home user to be processed, wherein the candidate recommendation data is text data or image data.
7. The smart home based user information recommendation method of any one of claims 1 to 6, wherein the step of performing user matching processing on a plurality of smart home users based on corresponding smart home device operation data to determine a user matching relationship among the plurality of smart home users comprises:
Extracting a plurality of intelligent home equipment operation data corresponding to a first intelligent home user, wherein the first intelligent home user and a second intelligent home user have a matching relationship, and the intelligent home equipment operation data is at least used for reflecting the use condition of the first intelligent home user on corresponding intelligent home equipment;
performing data decomposition operation on the plurality of intelligent home equipment operation data to form a plurality of local equipment operation data corresponding to each intelligent home equipment operation data, and performing screening operation on the plurality of local equipment operation data corresponding to each intelligent home equipment operation data to form a target equipment operation behavior corresponding to each local equipment operation data, wherein each local equipment operation data comprises a plurality of equipment operation behaviors;
performing key information mining operation on the target equipment operation behaviors corresponding to each piece of local equipment operation data respectively to output behavior key information representation vectors corresponding to each piece of local equipment operation data, and analyzing a first equipment operation representation vector corresponding to the first smart home user according to the behavior key information representation vectors;
Performing reinforcement operation on a first equipment operation representation vector corresponding to the first smart home user to form a second equipment operation representation vector corresponding to the first smart home user;
according to the second equipment operation characterization vector corresponding to the first smart home user, a matched third smart home user is screened out, and user matching operation on the second smart home user is conducted on the third smart home user, so that the first smart home user, the third smart home user and the second smart home user have a matching relationship with each other.
8. A user information recommendation system based on smart home is characterized by comprising:
the user matching processing module is used for carrying out user matching processing on a plurality of intelligent home users based on corresponding intelligent home equipment operation data so as to determine a user matching relationship among the plurality of intelligent home users;
the recommendation user determining module is used for determining information recommendation to-be-processed intelligent home users in the plurality of intelligent home users;
the matching user determining module is used for determining each matching intelligent home user corresponding to the intelligent home user to be processed in the plurality of intelligent home users based on the user matching relationship;
The user information recommending module is used for recommending the user information to the intelligent home users to be processed based on the user behavior characteristic information corresponding to the intelligent home users to be processed and the user behavior characteristic information corresponding to each matched intelligent home user, and determining target recommending data of the intelligent home users to be processed, wherein the user behavior characteristic information is used for reflecting the user behaviors of the corresponding intelligent home users.
9. The smart home-based user information recommendation system of claim 8, wherein the recommendation user determination module is specifically configured to:
for each intelligent home user in the plurality of intelligent home users, determining each matched intelligent home user corresponding to the intelligent home user based on the user matching relationship, and counting the number of the matched intelligent home users corresponding to the intelligent home user;
determining the corresponding intelligent home user as a candidate intelligent home user under the condition that the number of the corresponding matching intelligent home users is larger than or equal to the preset reference number;
and selecting one candidate intelligent home user from each determined candidate intelligent home user to mark the candidate intelligent home users as information recommendation to be processed.
10. The smart home-based user information recommendation system of claim 8, wherein the user information recommendation module is specifically configured to:
acquiring user behavior characteristic information corresponding to the smart home user to be processed to obtain first user behavior characteristic information, and acquiring user behavior characteristic information corresponding to each matched smart home user to obtain second user behavior characteristic information;
performing key information mining operation on the first user behavior feature information to form corresponding first user behavior characterization vectors, and performing key information mining operation on each piece of second user behavior feature information to form corresponding second user behavior characterization vectors;
and based on the first user behavior characterization vector and the second user behavior characterization vector, performing user information recommendation operation on the smart home user to be processed so as to determine target recommendation data of the smart home user to be processed from a plurality of candidate recommendation data.
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