Method and device for realizing simultaneous screening of multiple attributes based on user input keywords

Abstract

The embodiment of the present invention provides a method, device and electronic device for implementing simultaneous screening of multiple attributes based on user input keywords, belonging to the field of data processing technology, the method comprising: obtaining all attributes A contained in the screening box at one time; matching and calculating the input content S with the standard attribute value list Va to obtain the matching degree M(Va,S)=Fp(Pt); classifying the input content S under the corresponding attribute name; when detecting that the user clicks on the attribute value, recording the user's click behavior E, and updating the user selection state set U according to the clicked attribute value Va and its corresponding attribute name a; using a multi-factor comprehensive evaluation model G(a) to calculate the comprehensive weight of the selected attribute value under each attribute, and submitting the aggregated data P to the back-end interface for search and filtering operations. The present invention can perform simultaneous screening of multiple attributes in one filter, thereby improving the convenience of user screening.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and electronic device for implementing simultaneous screening of multiple attributes based on user input keywords. Background Art

[0002] Attribute filter is a user interface component that helps users quickly find information that meets specific conditions in a large amount of data. Attribute filter is a very common and important function in many application scenarios such as e-commerce, search engines, and database queries. Through attribute filter, users can select one or more attributes and their corresponding values ​​to narrow the search scope and improve search efficiency.

[0003] The existing technology has the following defects in the process of developing attribute filters:

[0004] 1. Slow response: After the user enters a keyword or selects an attribute value, the system responds in a long time, affecting the user experience.

[0005] 2. Low matching accuracy: After the user enters keywords, the matching results returned by the system are not accurate enough and may contain a large number of irrelevant attribute values.

[0006] 3. Complex combination screening of multiple attributes: When users select multiple attributes for combination screening, the system has difficulty processing complex logical relationships, which may lead to inaccurate results.

[0007] 4. Unfriendly user interface: The user interface is not designed reasonably, the operation is cumbersome, and it is difficult for users to quickly find the required filtering options.

[0008] The above problems become technical problems that need to be solved. Summary of the invention

[0009] In view of this, the embodiments of the present invention provide a method, device and electronic device for implementing simultaneous screening of multiple attributes based on user input keywords, which at least partially solve the problems existing in the prior art.

[0010] In a first aspect, an embodiment of the present invention provides a method for implementing simultaneous screening of multiple attributes based on keywords input by a user, comprising:

[0011] Obtain the original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in the filter box at one time, and construct a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to sort the original attribute value list Ve to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve;

[0012] Monitor the input content S of the user in the input box in real time, and construct a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] to match the input content S with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), where Sm(Va) is the similarity score between Va and S, M(Va,S) represents the matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is a preset threshold, and Fp is a matching degree calculation function;

[0013] According to the calculated matching degree M(Va,S), the input content S is classified into the corresponding attribute name, and the drop-down box list is updated in descending order according to the matching degree. The matched attribute values ​​are aggregated and displayed in units of attribute names. A click event is bound to each attribute value. The click event record format is E={aj,Vaj,t,W(Vaj)}, where aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient;

[0014] When it is detected that the user clicks on an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, Va is added to U, U=U∪{(a,Va)}; if Va is already in U, Va is removed from U, U=U\{(a,Va)}, where \ represents a removal operation;

[0015] After the user triggers the confirmation operation, all selected attribute values ​​are collected, and the multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute values ​​under each attribute. The attribute values ​​selected by the user are aggregated in units of attribute name, and the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} is submitted to the back-end interface for search and filtering operations.

[0016] According to a specific implementation of an embodiment of the present invention, the original attribute value list Ve is sorted by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2, ..., Vam}, including:

[0017] Calculate the heat score H(vej) for each attribute value:

[0018]

[0019] is the usage frequency of the attribute value vej in the past preset time period;

[0020] Calculate the preference score P(vej) of each attribute value based on the user's browsing history data and click behavior data;

[0021] The relevance score R(vej) of each attribute value is calculated based on the association between the attribute value vej and other attribute values.

[0022] According to a specific implementation of an embodiment of the present invention, the original attribute value list Ve is sorted by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2, ..., Vam}, and further includes:

[0023] Calculate the comprehensive score F(vej) for each attribute value vej:

[0024] , α1, β1, γ1 are weight coefficients

[0025] Sort all attribute values ​​vej from high to low according to the comprehensive score F(vej) to obtain the standard attribute value list Va.

[0026] According to a specific implementation of an embodiment of the present invention, the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] is constructed, and the input content S is matched with the standard attribute value list Va to obtain the matching degree M(Va,S)=Fp(Pt), including:

[0027] Calculate the similarity score Sm(Va,S):

[0028]

[0029] TF-IDF stands for term frequency-inverse document frequency;

[0030] Based on the matching degree between the attribute value Va and the input content S, the matching degree M(Va,S) is calculated;

[0031] A preset threshold T0 is set to determine whether the matching degree reaches an acceptable standard.

[0032] According to a specific implementation of an embodiment of the present invention, the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] is constructed, and the input content S is matched with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), and further includes:

[0033] Define the matching degree calculation function Fp(Pt) to calculate the final matching degree M(Va,S) according to the matching vector Pt:

[0034]

[0035] α1, β2, γ2, δ2 are weight coefficients, Lmax is the maximum input length, I(|S|≥T0) is the indicator function, which returns 1 if |S|≥T0, otherwise returns 0.

[0036] According to a specific implementation of an embodiment of the present invention, the input content S is classified into corresponding attribute names according to the calculated matching degree M(Va,S), and the drop-down box list is updated in descending order according to the matching degree, and the matched attribute values ​​are aggregated and displayed in units of attribute names, including:

[0037] After obtaining the matching degree M(Va,S) of each attribute value Va through the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] and the matching degree calculation function Fp(Pt), for each attribute value Va, its matching degree M(Va,S) is compared with the preset threshold T0;

[0038] If M(Va,S)≥T0, the input content S is classified into the attribute name to which the attribute value belongs;

[0039] Collect all attribute values ​​with matching degree M(Va,S)≥T0, and sort these attribute values ​​from high to low according to the matching degree M(Va,S);

[0040] Update the drop-down list to display the sorted attribute values.

[0041] According to a specific implementation of an embodiment of the present invention, when it is detected that a user clicks an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a, including:

[0042] Define a user selection state set U, which is used to record the attribute value currently selected by the user and its corresponding attribute name. In the initial state, U is empty;

[0043] Define the click event record format E={aj,Vaj,t,W(Vaj)};

[0044] Monitor the user's click event in the drop-down box. When the user clicks a certain attribute value Va, record the click event E;

[0045] Check whether the clicked attribute value Va is already in the user selection state set U. If Va is not in U, add Va to U and update U to U=U∪{(a,Va)}. If Va is already in U, remove Va from U and update U to U=U\{(a,Va)}.

[0046] According to a specific implementation of an embodiment of the present invention, the multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute value under each attribute, including:

[0047] Define the multi-factor comprehensive evaluation model G(a):

[0048]

[0049] is the sum of the products of the weights and heat scores of all selected attribute values, It is the sum of the weights of all selected attribute values.

[0050] In a second aspect, an embodiment of the present invention provides a device for implementing simultaneous screening of multiple attributes based on keywords input by a user, comprising:

[0051] An acquisition module obtains the original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in the filter box at one time, and sorts the original attribute value list Ve by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve;

[0052] The matching module monitors the input content S of the user in the input box in real time, and constructs a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] to match the input content S with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), where Sm(Va) is the similarity score between Va and S, M(Va,S) represents the matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is a preset threshold, and Fp is a matching degree calculation function;

[0053] The classification module classifies the input content S into the corresponding attribute name according to the calculated matching degree M(Va,S), updates the drop-down box list in descending order according to the matching degree, aggregates and displays the matched attribute values ​​in units of attribute names, binds click events to each attribute value, and the click event record format is E={aj,Vaj,t,W(Vaj)}, where aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient;

[0054] Update module, when it is detected that the user clicks on an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, Va is added to U, U=U∪{(a,Va)}; if Va is already in U, Va is removed from U, U=U\{(a,Va)}, where \ represents a removal operation;

[0055] In the submission module, after the user triggers the confirmation operation, all selected attribute values ​​are collected, and the comprehensive weight of the selected attribute values ​​under each attribute is calculated using the multi-factor comprehensive evaluation model G(a). The attribute values ​​selected by the user are aggregated in units of attribute names, and the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} is submitted to the back-end interface for search and filtering operations.

[0056] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0057] at least one processor; and,

[0058] a memory communicatively connected to the at least one processor; wherein,

[0059] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of realizing simultaneous screening of multiple attributes based on user input keywords in any of the aforementioned first aspects or any implementation of the first aspect.

[0060] In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the method of realizing simultaneous screening of multiple attributes based on user input keywords in the aforementioned first aspect or any implementation method of the first aspect.

[0061] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method for simultaneous screening of multiple attributes based on user input keywords in the aforementioned first aspect or any implementation of the first aspect.

[0062] The scheme for implementing simultaneous screening of multiple attributes based on user input keywords in an embodiment of the present invention includes: obtaining an original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in a screening box at one time, and sorting the original attribute value list Ve by constructing a ternary filtering function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve; monitoring the user's input content S in the input box in real time, and constructing a matching vector Pt=[Sm(Va, S), M(Va, S), |S|, T0 ], match the input content S with the standard attribute value list Va to obtain the matching degree M(Va,S)=Fp(Pt), Sm(Va) is the similarity score between Va and S, M(Va,S) represents the matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is the preset threshold, and Fp is the matching degree calculation function; according to the calculated matching degree M(Va,S), the input content S is classified into the corresponding attribute name, and the drop-down box list is updated in descending order according to the matching degree, the matched attribute values ​​are aggregated and displayed in units of attribute names, and a click event is bound to each attribute value. The click event record format is E={aj,Vaj,t,W(Vaj)}, aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient; when it is detected that the user clicks on the attribute value, the user's click behavior E is recorded, and the user's selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, Va is added to U, U=U∪{(a,Va)}, if Va is already in U, Va is removed from U, U=U\{(a,Va)}, \ represents the removal operation; after the user triggers the confirmation operation, all selected attribute values ​​are collected, and the multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute value under each attribute, and the attribute values ​​selected by the user are aggregated in units of attribute names, and the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} is submitted to the backend interface for search and filtering operations. This solution has the following beneficial effects:

[0063] 1. Efficient user input matching

[0064] By constructing the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0], the system can monitor the user input content S in real time and quickly match the attribute values ​​in the standard attribute value list Va. The matching algorithm is used to calculate the similarity score Sm(Va,S), and the matching degree M(Va,S) and the preset threshold T0 are combined to ensure the accuracy of the matching results.

[0065] 2. Dynamic sorting and display

[0066] Sort the attribute values ​​according to the matching degree M(Va,S), and update the drop-down list in real time to display the most relevant attribute values, improving the user's search efficiency. Support users to select multiple attribute values, providing more flexible filtering conditions.

[0067] 3. Dynamic management of user selection status

[0068] By recording the click event E={aj,Vaj,t,W(Vaj)}, the system can accurately record the user's click behavior. According to the user's click behavior, the user selection state set U is dynamically updated to ensure that every step of the user's selection is recorded and responded to by the system.

[0069] 4. Comprehensive evaluation of multiple factors

[0070] The multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute value under each attribute, combined with the dynamic adjustment value W(Vaj) of the attribute value and the heat score H(Vaj). Based on the comprehensive weight, the system can intelligently recommend attribute values ​​that users may be interested in and provide personalized screening suggestions.

[0071] 5. Improve user satisfaction and system efficiency

[0072] Through the user's historical selection behavior and real-time input content, the system can provide personalized screening results and improve user satisfaction. With modular design and microservice architecture, the system is easy to expand and maintain, adapting to changing business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0074] Figure 1A schematic flow chart of a method for implementing simultaneous screening of multiple attributes based on user input keywords provided by an embodiment of the present invention;

[0075] Figure 2 A flowchart of another method for implementing simultaneous screening of multiple attributes based on user input keywords provided by an embodiment of the present invention;

[0076] Figure 3 A schematic diagram of the structure of a device for implementing simultaneous screening of multiple attributes based on user input keywords provided in an embodiment of the present invention;

[0077] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0079] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0080] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0081] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0082] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0083] The disclosed embodiment provides a method for implementing simultaneous screening of multiple attributes based on user input keywords. The method for implementing simultaneous screening of multiple attributes based on user input keywords provided in this embodiment can be performed by a computing device, which can be implemented as software, or as a combination of software and hardware, and the computing device can be integrated in a server, terminal device, etc.

[0084] See also Figure 1 and Figure 2 The present disclosure provides a method for implementing simultaneous screening of multiple attributes based on user input keywords, including:

[0085] S101, obtain the original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in the filter box at one time, and construct a ternary filtering function group Fs={H(Ve), P(Ve), R(Ve)} to sort the original attribute value list Ve to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve.

[0086] The original attribute value list Ve={ve1,ve2,...,ven} can be defined to represent the original attribute value list, where n is the number of attribute values.

[0087] Define a ternary filter function group Fs:

[0088] H(Ve): represents the heat score function of the attribute value.

[0089] P(Ve): represents the user's preference score function for attribute values.

[0090] R(Ve): represents the relevance score function of the attribute value.

[0091] Calculate the heat score H(vej) for each attribute value:

[0092] ,in It is the usage frequency of the attribute value vej in the past period of time.

[0093] Calculate the preference score P(vej) for each attribute value:

[0094] P(vej) can be calculated based on the user's browsing history, click behavior, purchase history, etc. For example, if a user frequently selects a certain attribute value, the preference score for that attribute value will be higher.

[0095] Calculate the relevance score R(vej) for each attribute value:

[0096] R(vej) can be calculated based on how well an attribute value is associated with other attribute values. For example, some attribute values ​​may often appear together, so their relevance score will be high.

[0097] Calculate the comprehensive score F(vej) for each attribute value:

[0098] , where α1, β1, γ1 are weight coefficients

[0099] Sort all attribute values ​​vej from high to low according to the comprehensive score F(vej) to obtain the standard attribute value list Va.

[0100] By constructing a ternary filtering function group Fs={H(Ve), P(Ve), R(Ve)} and comprehensively scoring and sorting the original attribute value list Ve, the standard attribute value list Va can be obtained, thereby providing a more reasonable and personalized user screening experience.

[0101] S102, monitor the user's input content S in the input box in real time, and construct a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] to match the input content S with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), where Sm(Va) is the similarity score between Va and S, M(Va,S) represents the matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is the preset threshold, and Fp is the matching degree calculation function.

[0102] A standard attribute value list Va can be defined: Va={Va1, Va2, ..., Vam} represents a sorted standard attribute value list, where m is the number of attribute values.

[0103] Define input content S: S represents the content entered by the user in the input box.

[0104] Construct the matching vector Pt: Pt=[Sm(Va,S),M(Va,S),|S|,T0] is a vector containing four elements used for matching calculation.

[0105] Calculate the similarity score Sm(Va,S):

[0106] Sm(Va,S) represents the similarity score between the attribute value Va and the input content S:

[0107] Sm(Va,S)=cos(TF-IDF(Va),TF-IDF(S))

[0108] Among them, TF-IDF means term frequency-inverse document frequency.

[0109] M(Va,S) represents the matching degree between the attribute value Va and the input content S. The matching degree can be calculated based on the similarity score and other factors. |S| represents the length of the input string S. Set the preset threshold T0: T0 is a preset threshold used to determine whether the matching degree meets the acceptable standard.

[0110] Fp(Pt) is a function used to calculate the final matching degree M(Va,S) based on the matching vector Pt. The matching degree calculation function Fp(Pt) is defined as:

[0111]

[0112] Where: α1, β2, γ2, δ2 are weight coefficients, Lmax is the maximum input length. I(|S|≥T0) is an indicator function that returns 1 if |S|≥T0, otherwise returns 0.

[0113] By constructing a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] and matching the input content S with the standard attribute value list Va, the matching degree M(Va,S) of each attribute value can be obtained, thereby providing a more accurate and personalized user screening experience.

[0114] S103, according to the calculated matching degree M(Va,S), classify the input content S into the corresponding attribute name, and update the drop-down box list in descending order according to the matching degree, aggregate and display the matched attribute values ​​in units of attribute name, bind a click event to each attribute value, and the click event record format is E={aj,Vaj,t,W(Vaj)}, aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient.

[0115] By constructing the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] and the matching degree calculation function Fp(Pt), the matching degree M(Va,S)M(Va,S) of each attribute value Va is calculated. For each attribute value Va, its matching degree M(Va,S) is compared with the preset threshold T0. If M(Va,S)≥T0, the input content SS is classified under the attribute name to which the attribute value belongs.

[0116] Collect all attribute values ​​with matching degree M(Va,S)≥T0, and sort these attribute values ​​from high to low according to the matching degree M(Va,S). Update the drop-down list to display the sorted attribute values, and aggregate them by attribute name.

[0117] Bind a click event to each attribute value and record the user's click behavior. The format of the click event record is E={aj,Vaj,t,W(Vaj)}, where aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, and t represents the click time. W(Vaj) represents the dynamic adjustment value of the jth attribute value, and the calculation formula is:

[0118]

[0119] Among them, C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient.

[0120] S104, when it is detected that the user clicks on an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, Va is added to U, U=U∪{(a,Va)}; if Va is already in U, Va is removed from U, U=U\{(a,Va)}, where \ represents a removal operation.

[0121] A user selection state set U can be defined: U is a set that records the attribute values ​​currently selected by the user and their corresponding attribute names. For example, U={(a1,Va11),(a2,Va21),(a1,Va12)}.

[0122] Listen to the user's click event in the drop-down box. When the user clicks a certain attribute value Va, record the click event E. Check whether the clicked attribute value Va is already in the user selection state set U.

[0123] If Va is not in U, add Va to U and update U to U=U∪{(a,Va)}. If Va is already in U, remove Va from U and update U to U=U\{(a,Va)}.

[0124] By recording the user's click behavior E and updating the user selection state set U according to the clicked attribute value Va and its corresponding attribute name a, the user's screening state can be dynamically managed to provide a more flexible and personalized user interaction experience. This method can ensure that every step of the user's selection is recorded and responded to by the system, thereby improving user satisfaction and system efficiency.

[0125] S105, after the user triggers the confirmation operation, all selected attribute values ​​are collected, and the comprehensive weight of the selected attribute value under each attribute is calculated using the multi-factor comprehensive evaluation model G(a), the attribute values ​​selected by the user are aggregated in units of attribute name, and the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} is submitted to the back-end interface for search and filtering operations.

[0126] The dynamic adjustment value W(Vaj) of the attribute value can be defined as: W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), where: F(Vaj) is the influence value of the jth attribute calculated based on the ternary filter function group Fs={H(Ve),P(Ve),R(Ve)}. C(Vaj) is the influence value of the user's historical selection behavior on Vaj. λ is the adjustment coefficient, which is usually between 0 and 1.

[0127] Define the heat score H(Vaj) of the attribute value:

[0128] H(Vaj)=log(1+f(Vaj)), where f(Vaj) is the frequency of use of the attribute value Vaj in the past period of time.

[0129] Define the multi-factor comprehensive evaluation model G(a):

[0130] ,in:

[0131] It is the sum of the products of the weights and heat scores of all selected attribute values. It is the sum of the weights of all selected attribute values.

[0132] By using the multi-factor comprehensive evaluation model G(a) to calculate the comprehensive weight of the selected attribute value under each attribute, the user's screening conditions can be evaluated more comprehensively, providing more accurate search and filtering results. This method can combine multiple factors (such as weight, heat score, etc.) for comprehensive evaluation, thereby improving the intelligence level of the system and user experience.

[0133] See also Figure 2 The present invention provides a method for performing simultaneous multi-attribute screening in one filter. The present invention can avoid the implementation of multiple selectors and provide great convenience to users. Since all attribute value lists are prepared at one time, it is possible to respond to keywords input by users in real time. The steps of the technical solution of the present invention include:

[0134] 1. Get a list of attribute values ​​for all attributes at once.

[0135] 2. Provide users with a basic filter, which includes an input box and a drop-down list showing attribute values.

[0136] 3. By default, when the user does not input anything, multiple attributes are aggregated and displayed in order by attribute.

[0137] 4. Monitor the content entered by the user in the input box in real time, use the content entered by the user to perform fuzzy matching on the attribute value list of multiple attributes, and summarize the keywords entered by the user under the attribute named keyword, so that fuzzy matching can be performed on the content, title or description based on the keyword.

[0138] 5. The attributes that match the attribute value are aggregated and displayed in the drop-down box in units of attribute name.

[0139] 6. Bind a click event to each attribute value. When the user clicks on a different attribute value, add or cancel the attribute value according to the attribute name of the attribute value clicked.

[0140] 7. When the user clicks the OK button, the attribute values ​​selected by the user are aggregated by attribute name and provided to the interface for search.

[0141] 8. When the user enters a keyword again, the attribute value selected by the user that was previously collected is echoed in the attribute list to serve as a prompt to the user.

[0142] According to a specific implementation of an embodiment of the present invention, the original attribute value list Ve is sorted by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2, ..., Vam}, including:

[0143] Calculate the heat score H(vej) for each attribute value:

[0144]

[0145] is the usage frequency of the attribute value vej in the past preset time period;

[0146] Calculate the preference score P(vej) of each attribute value based on the user's browsing history data and click behavior data;

[0147] The relevance score R(vej) of each attribute value is calculated based on the association between the attribute value vej and other attribute values.

[0148] According to a specific implementation of an embodiment of the present invention, the original attribute value list Ve is sorted by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2, ..., Vam}, and further includes:

[0149] Calculate the comprehensive score F(vej) for each attribute value vej:

[0150] , α1, β1, γ1 are weight coefficients

[0151] Sort all attribute values ​​vej from high to low according to the comprehensive score F(vej) to obtain the standard attribute value list Va.

[0152] According to a specific implementation of an embodiment of the present invention, the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] is constructed, and the input content S is matched with the standard attribute value list Va to obtain the matching degree M(Va,S)=Fp(Pt), including:

[0153] Calculate the similarity score Sm(Va,S):

[0154]

[0155] TF-IDF stands for term frequency-inverse document frequency;

[0156] Based on the matching degree between the attribute value Va and the input content S, the matching degree M(Va,S) is calculated;

[0157] A preset threshold T0 is set to determine whether the matching degree reaches an acceptable standard.

[0158] According to a specific implementation of an embodiment of the present invention, the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] is constructed, and the input content S is matched with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), and further includes:

[0159] Define the matching degree calculation function Fp(Pt) to calculate the final matching degree M(Va,S) according to the matching vector Pt:

[0160]

[0161] α1, β2, γ2, δ2 are weight coefficients, Lmax is the maximum input length, I(|S|≥T0) is the indicator function, which returns 1 if |S|≥T0, otherwise returns 0.

[0162] According to a specific implementation of an embodiment of the present invention, the input content S is classified into corresponding attribute names according to the calculated matching degree M(Va,S), and the drop-down box list is updated in descending order according to the matching degree, and the matched attribute values ​​are aggregated and displayed in units of attribute names, including:

[0163] After obtaining the matching degree M(Va,S) of each attribute value Va through the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] and the matching degree calculation function Fp(Pt), for each attribute value Va, its matching degree M(Va,S) is compared with the preset threshold T0;

[0164] If M(Va,S)≥T0, the input content S is classified into the attribute name to which the attribute value belongs;

[0165] Collect all attribute values ​​with matching degree M(Va,S)≥T0, and sort these attribute values ​​from high to low according to the matching degree M(Va,S);

[0166] Update the drop-down list to display the sorted attribute values.

[0167] According to a specific implementation of an embodiment of the present invention, when it is detected that a user clicks an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a, including:

[0168] Define a user selection state set U, which is used to record the attribute value currently selected by the user and its corresponding attribute name. In the initial state, U is empty;

[0169] Define the click event record format E={aj,Vaj,t,W(Vaj)};

[0170] Monitor the user's click event in the drop-down box. When the user clicks a certain attribute value Va, record the click event E;

[0171] Check whether the clicked attribute value Va is already in the user selection state set U. If Va is not in U, add Va to U and update U to U=U∪{(a,Va)}. If Va is already in U, remove Va from U and update U to U=U\{(a,Va)}.

[0172] According to a specific implementation of an embodiment of the present invention, the multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute value under each attribute, including:

[0173] Define the multi-factor comprehensive evaluation model G(a):

[0174]

[0175] is the sum of the products of the weights and heat scores of all selected attribute values, It is the sum of the weights of all selected attribute values.

[0176] Corresponding to the above method embodiment, see Figure 3 The embodiment of the present invention further discloses a device 30 for implementing simultaneous screening of multiple attributes based on keywords input by a user, comprising:

[0177] The acquisition module 301 acquires the original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in the filter box at one time, and constructs a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to sort the original attribute value list Ve to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve;

[0178] The matching module 302 monitors the input content S of the user in the input box in real time, and performs matching calculation on the input content S and the standard attribute value list Va by constructing a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] to obtain a matching degree M(Va,S)=Fp(Pt), where Sm(Va) is a similarity score between Va and S, M(Va,S) represents a matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is a preset threshold, and Fp is a matching degree calculation function;

[0179] The classification module 303 classifies the input content S into the corresponding attribute name according to the calculated matching degree M(Va,S), updates the drop-down box list in descending order according to the matching degree, aggregates and displays the matched attribute values ​​in units of attribute names, binds a click event to each attribute value, and the click event record format is E={aj,Vaj,t,W(Vaj)}, where aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient;

[0180] Update module 304, when detecting that the user clicks on an attribute value, records the user's click behavior E, and updates the user selection state set U according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, add Va to U, U=U∪{(a,Va)}; if Va is already in U, remove Va from U, U=U\{(a,Va)}, where \ represents a removal operation;

[0181] Submission module 305, after the user triggers the confirmation operation, collects all selected attribute values, uses the multi-factor comprehensive evaluation model G(a) to calculate the comprehensive weight of the selected attribute values ​​under each attribute, aggregates the attribute values ​​selected by the user in units of attribute names, and submits the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} to the back-end interface for search and filtering operations.

[0182] See also Figure 4 The embodiment of the present invention further provides an electronic device 60, the electronic device comprising:

[0183] at least one processor; and,

[0184] a memory communicatively connected to the at least one processor; wherein,

[0185] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for implementing simultaneous screening of multiple attributes based on user input keywords in the aforementioned method embodiment.

[0186] An embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the aforementioned method embodiment.

[0187] An embodiment of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method of realizing simultaneous screening of multiple attributes based on user input keywords in the aforementioned method embodiment.

[0188] Reference below Figure 4 , which shows a schematic diagram of the structure of an electronic device 60 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0189] like Figure 4As shown, the electronic device 60 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0190] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, a gage, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 60 is shown with various devices, but it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have instead.

[0191] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0192] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for implementing simultaneous screening of multiple attributes based on user input keywords, characterized in that: include: Obtain the original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in the filter box at one time, and construct a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to sort the original attribute value list Ve to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve; Monitor the input content S of the user in the input box in real time, and construct a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] to match the input content S with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), where Sm(Va) is the similarity score between Va and S, M(Va,S) represents the matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is a preset threshold, and Fp is a matching degree calculation function; According to the calculated matching degree M(Va,S), the input content S is classified into the corresponding attribute name, and the drop-down box list is updated in descending order according to the matching degree. The matched attribute values ​​are aggregated and displayed in units of attribute names. A click event is bound to each attribute value. The click event record format is E={aj,Vaj,t,W(Vaj)}, where aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient; When it is detected that the user clicks on an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, Va is added to U, U=U∪{(a,Va)}; if Va is already in U, Va is removed from U, U=U\{(a,Va)}, where \ represents a removal operation; After the user triggers the confirmation operation, all selected attribute values ​​are collected, and the multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute values ​​under each attribute. The attribute values ​​selected by the user are aggregated in units of attribute name, and the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} is submitted to the back-end interface for search and filtering operations.

2. The method according to claim 1, characterized in that The original attribute value list Ve is sorted by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2, ..., Vam}, including: Calculate the heat score H(vej) for each attribute value: is the usage frequency of the attribute value vej in the past preset time period; Calculate the preference score P(vej) of each attribute value based on the user's browsing history data and click behavior data; The relevance score R(vej) of each attribute value is calculated based on the association between the attribute value vej and other attribute values.

3. The method according to claim 2, characterized in that The method constructs a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)}, sorts the original attribute value list Ve, and obtains a standard attribute value list Va={Va1, Va2, ..., Vam}, and further includes: Calculate the comprehensive score F(vej) for each attribute value vej: , α1, β1, γ1 are weight coefficients Sort all attribute values ​​vej from high to low according to the comprehensive score F(vej) to obtain the standard attribute value list Va.

4. The method according to claim 3, characterized in that The matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] is constructed, and the input content S is matched with the standard attribute value list Va to obtain the matching degree M(Va,S)=Fp(Pt), including: Calculate the similarity score Sm(Va,S): TF-IDF stands for term frequency-inverse document frequency; Based on the matching degree between the attribute value Va and the input content S, the matching degree M(Va,S) is calculated; A preset threshold T0 is set to determine whether the matching degree reaches an acceptable standard.

5. The method according to claim 4, characterized in that The method constructs a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0], performs matching calculation on the input content S and the standard attribute value list Va, and obtains a matching degree M(Va,S)=Fp(Pt), and further includes: Define the matching degree calculation function Fp(Pt) to calculate the final matching degree M(Va,S) according to the matching vector Pt: α1, β2, γ2, δ2 are weight coefficients, Lmax is the maximum input length, I(|S|≥T0) is the indicator function, which returns 1 if |S|≥T0, otherwise returns 0.

6. The method according to claim 5, characterized in that According to the calculated matching degree M(Va,S), the input content S is classified into the corresponding attribute name, and the drop-down box list is updated in descending order according to the matching degree, and the matched attribute values ​​are aggregated and displayed in units of attribute name, including: After obtaining the matching degree M(Va,S) of each attribute value Va through the matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] and the matching degree calculation function Fp(Pt), for each attribute value Va, its matching degree M(Va,S) is compared with the preset threshold T0; If M(Va,S)≥T0, the input content S is classified into the attribute name to which the attribute value belongs; Collect all attribute values ​​with matching degree M(Va,S)≥T0, and sort these attribute values ​​from high to low according to the matching degree M(Va,S); Update the drop-down list to display the sorted attribute values.

7. The method according to claim 6, characterized in that When it is detected that the user clicks on an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a, including: Define a user selection state set U, which is used to record the attribute value currently selected by the user and its corresponding attribute name. In the initial state, U is empty; Define the click event record format E={aj,Vaj,t,W(Vaj)}; Monitor the user's click event in the drop-down box. When the user clicks a certain attribute value Va, record the click event E; Check whether the clicked attribute value Va is already in the user selection state set U. If Va is not in U, add Va to U and update U to U=U∪{(a,Va)}. If Va is already in U, remove Va from U and update U to U=U∖{(a,Va)}.

8. The method according to claim 7, characterized in that The multi-factor comprehensive evaluation model G(a) is used to calculate the comprehensive weight of the selected attribute value under each attribute, including: Define the multi-factor comprehensive evaluation model G(a): is the sum of the products of the weights and heat scores of all selected attribute values, It is the sum of the weights of all selected attribute values.

9. A device for implementing simultaneous screening of multiple attributes based on user input keywords, characterized in that: include: An acquisition module obtains the original attribute value list Ve={Ve1, Ve2,..., Vem} of all attributes A={a1, a2,..., an} contained in the filter box at one time, and sorts the original attribute value list Ve by constructing a ternary filter function group Fs={H(Ve), P(Ve), R(Ve)} to obtain a standard attribute value list Va={Va1, Va2,..., Vam}, where H(Ve)=log(1+f(Ve)) represents the popularity score of the attribute value Ve, f(Ve) is the usage frequency of Ve in the past preset time period, P(Ve) represents the user's preference score for the attribute value Ve, and R(Ve) represents the relevance score of the attribute value Ve; The matching module monitors the input content S of the user in the input box in real time, and constructs a matching vector Pt=[Sm(Va,S),M(Va,S),|S|,T0] to match the input content S with the standard attribute value list Va to obtain a matching degree M(Va,S)=Fp(Pt), where Sm(Va) is the similarity score between Va and S, M(Va,S) represents the matching degree between the attribute value Va and the input string S, |S| represents the length of the input string, T0 is a preset threshold, and Fp is a matching degree calculation function; The classification module classifies the input content S into the corresponding attribute name according to the calculated matching degree M(Va,S), updates the drop-down box list in descending order according to the matching degree, aggregates and displays the matched attribute values ​​in units of attribute names, binds click events to each attribute value, and the click event record format is E={aj,Vaj,t,W(Vaj)}, where aj represents the jth attribute, Vaj represents the attribute value of the jth attribute, t represents the click time, W(Vaj) represents the dynamic adjustment value of the jth attribute value, W(Vaj)=λ*F(Vaj)+(1-λ)*C(Vaj), C(Vaj) is the impact value of the user's historical selection behavior on Vaj, F(Vaj) is the impact value of the jth attribute calculated based on the ternary filter function group Fs, and λ is the adjustment coefficient; Update module, when it is detected that the user clicks on an attribute value, the user's click behavior E is recorded, and the user selection state set U is updated according to the clicked attribute value Va and its corresponding attribute name a. If Va is not in U, Va is added to U, U=U∪{(a,Va)}; if Va is already in U, Va is removed from U, U=U\{(a,Va)}, where \ represents a removal operation; In the submission module, after the user triggers the confirmation operation, all selected attribute values ​​are collected, and the comprehensive weight of the selected attribute values ​​under each attribute is calculated using the multi-factor comprehensive evaluation model G(a). The attribute values ​​selected by the user are aggregated in units of attribute names, and the aggregated data P={(a1,G(a1)),(a2,G(a2)),...,(al,G(al))} is submitted to the back-end interface for search and filtering operations.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for realizing simultaneous screening of multiple attributes based on user input keywords as described in any one of claims 1 to 8.

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