Information recommendation method, electronic equipment and storage medium

Abstract

The invention provides an information recommendation method, electronic equipment and a storage medium, and the method comprises the steps: carrying out the matching of each piece of main body attribute information of a matched main body and each piece of main body feature information of a to-be-confirmed main body, so as to determine a first main body corresponding to each piece of main body attribute information; determining the main body attribute information as first main body attribute information, wherein the matching degree between the main body attribute information and any main body feature information of any first main body is greater than a preset matching degree threshold value; determining the subject attribute information which does not belong to the first subject attribute information as second subject attribute information; determining third subject attribute information, wherein the combination of the third subject attribute information and each piece of second subject attribute information accords with a preset information combination rule; and according to each piece of second subject attribute information and the corresponding third subject attribute information, determining a target subject from the plurality of first subjects, so that the matching accuracy of the subject feature information of the determined target subject and the subject attribute information of the matched subject is improved.

Description

Background Art

[0002] When a user chooses a vehicle to purchase, he or she usually informs the vehicle dealer of his or her personal needs and preferences. The vehicle dealer then uses the information provided by the user to select vehicle information that matches the user's information from among a number of vehicles and pushes the vehicle information to the user for selection.

[0003] At present, most of the information recommendation methods provide vehicle information to users through sales staff. Due to the human factor, this method requires users to go to dealers for on-site inspection, which is not convenient for users to choose online. In addition, the existing information recommendation methods generally perform semantic retrieval and recognition of user information directly with each vehicle information. When a certain user information is successfully matched with a certain vehicle information, the vehicle information will be pushed to the user, but it often ignores whether the matching results of other user information with the vehicle information are correct. Therefore, the amount of vehicle information retrieved is too much and the retrieved vehicle information may have errors, and the user's intention to purchase the vehicle cannot be accurately determined. Summary of the invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] According to one aspect of the present application, there is provided an information recommendation method, which is applied to an information recommendation system, wherein the information recommendation system stores subject identifications and subject feature information of a number of subjects to be confirmed, and each subject feature information corresponds to a subject feature type;

[0006] The information recommendation method described in this application includes the following steps:

[0007] Step S100, obtaining some subject attribute information of the matching subject;

[0008] Step S200: Match the subject attribute type corresponding to each subject attribute information with the subject feature type corresponding to each subject feature information, so as to determine the first subject corresponding to each subject attribute information from a plurality of subjects to be confirmed;

[0009] Step S300: determining subject attribute information whose matching degree with any subject feature information of any first subject is greater than a preset matching degree threshold as first subject attribute information;

[0010] Step S400: determining subject attribute information that does not belong to the first subject attribute information among the plurality of subject attribute information as second subject attribute information;

[0011] Step S500: According to the subject attribute type corresponding to each second subject attribute information, determine from a plurality of subject attribute information the third subject attribute information that is combined with each second subject attribute information and meets a preset information combination rule;

[0012] Step S600: Determine a target subject from a plurality of first subjects according to each second subject attribute information and its corresponding third subject attribute information, and output the subject identification and subject characteristic information of the target subject.

[0013] In an exemplary embodiment of the present application, step S100 includes:

[0014] Step S110: Obtain the attribute information of each subject of the matching subject to obtain a subject attribute information list A=(A1, A2, ..., A m ,...,A n ), where m = 1, 2, ..., n, and n is the number of subject attribute information of the matching subject; A m The mth subject attribute information of the matching subject.

[0015] In an exemplary embodiment of the present application, step S200 includes:

[0016] Step S210: Obtain the subject attribute type corresponding to each subject attribute information to obtain a subject attribute type list B = (B1, B2, ..., B m ,...,B n ), where B m The subject attribute type corresponding to the mth subject attribute information of the matching subject;

[0017] Step S220: Obtain the subject feature information of each subject to be confirmed, so as to obtain a subject feature information list set C = (C1, C2, ..., C i ,...,C j );C i =(C i1 ,C i2 ,...,C ie ,...,C if(i) ); where i = 1, 2, ..., j; j is the number of entities to be confirmed; C i is the list of subject feature information corresponding to the i-th subject to be confirmed; e=1,2,...,f(i); f(i) is the number of subject feature information of the i-th subject to be confirmed; C ie is the e-th subject feature information of the i-th subject to be confirmed;

[0018] Step S230: Obtain the subject feature type corresponding to each subject feature information to obtain a subject feature type list set D = (D1, D2, ..., D i ,...,D j );D i =(D i1 ,Di2 ,...,D ie ,...,D if(i) ), where D i is the list of subject feature types corresponding to the i-th subject to be confirmed; D ie C ie The corresponding subject feature type;

[0019] Step S240, traverse the list set D of subject feature types, if D ie With B m The same, and C ie With A m If the matching degree of C is greater than the preset matching degree threshold, ie Confirmed as A m The corresponding first feature information, and the i-th subject to be confirmed is determined as A m The corresponding first subject.

[0020] In an exemplary embodiment of the present application, step S300 includes:

[0021] Step S310, traverse each subject attribute information, if B m With D ie The same, and A m With C ie If the matching degree of A is greater than the preset matching degree threshold, m Determined as the first subject attribute information.

[0022] In an exemplary embodiment of the present application, step S400 includes:

[0023] Step S410: traverse each subject attribute information, if B m is different from any of the subject feature types in the subject feature type list set D, or A m If the matching degree with each subject feature information in the subject feature information list set C is less than or equal to the preset matching degree threshold, then A m Determined as the second subject attribute information.

[0024] In an exemplary embodiment of the present application, step S500 includes:

[0025] Step S510: Obtain the subject attribute type corresponding to each second subject attribute information to obtain a second subject attribute type list E=(E1, E2, ..., E g ,...,E h ), where g = 1, 2, ..., h, and h is the number of second subject attribute information; E g is the subject attribute type corresponding to the g-th second subject attribute information;

[0026] Step S520, traverse the subject attribute type list B, if E g With B m There is a preset combination relationship, and E g With B m If the combination of meets the preset information combination rules, then A m The third subject attribute information corresponding to the g-th second subject attribute information is determined.

[0027] In an exemplary embodiment of the present application, step S600 includes:

[0028] Step S610: Combine each second subject attribute information with its corresponding third subject attribute information to obtain a plurality of fourth subject attribute information;

[0029] Step S620: Match each fourth subject attribute information with each subject feature information of each first subject to obtain a matching degree between each fourth subject attribute information and each subject feature information of each first subject;

[0030] Step S630: if the matching degree between any subject characteristic information of the first subject and any fourth subject attribute information is greater than a preset matching degree threshold, the subject characteristic information is determined as the second characteristic information;

[0031] Step S640: determine a target subject from a plurality of first subjects according to the number of second feature information and the number of first feature information corresponding to the first subjects, and output the subject identification and subject feature information of the target subject.

[0032] In an exemplary embodiment of the present application, step S640 includes:

[0033] Step S641: De-duplicate the first characteristic information and the second characteristic information corresponding to each first subject to obtain the target characteristic information corresponding to each first subject;

[0034] Step S642: determine the ratio of the amount of target characteristic information corresponding to each first subject to the amount of subject characteristic information corresponding to the first subject as the target value corresponding to the first subject;

[0035] Step S643: determine the first subject whose target value is greater than the preset target threshold as the target subject, and output the subject identification and subject characteristic information of the target subject.

[0036] The present invention has at least the following beneficial effects:

[0037] The information recommendation method of the present invention matches the subject attribute type corresponding to each subject attribute information of the matching subject with the subject feature type corresponding to each subject feature information of a plurality of subjects to be confirmed, determines a first subject corresponding to each subject attribute information from a plurality of subjects to be confirmed, and then determines subject attribute information whose matching degree with any subject feature information of any first subject is greater than a preset matching degree threshold as the first subject attribute information, determines subject attribute information that does not belong to the first subject attribute information among the plurality of subject attribute information as the second subject attribute information, and determines, according to the subject attribute type corresponding to each second subject attribute information, a combination of subject attribute information corresponding to each second subject attribute information from the plurality of subject attribute information. The method further comprises the following steps: first, determining a target subject from among the plurality of first subjects according to each second subject attribute information and its corresponding third subject attribute information, outputting the subject identification and subject characteristic information of the target subject, and determining the first subject that meets the single matching rule by performing a single matching between the subject characteristic information of the plurality of subjects to be confirmed and the subject attribute information, and then performing a two-stage matching between the subject characteristic information of the first subject and a combination of the plurality of second subject attribute information and the third subject attribute information, so as to improve the matching accuracy between the subject characteristic information of the determined target subject and the subject attribute information of the matching subject, so as to push more accurate subject characteristic information to the matching subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0039] Figure 1 The present invention provides a flowchart of an information recommendation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] An information recommendation method is applied to an information recommendation system. The information recommendation system stores subject identifications and subject feature information of a number of subjects to be confirmed, and each subject feature information corresponds to a subject feature type.

[0042] The information recommendation system is used to push the subject identification (which may be the model of the vehicle for sale) and subject characteristic information (which may be the vehicle information and applicable scope information of the vehicle for sale, such as vehicle color, vehicle price, gender of applicable users, age of applicable users, number of people in applicable families, etc.) of the subject to be confirmed (which may be the vehicle for sale) that matches the subject attribute information (which may be the user's intention preference information or personal basic information, such as the user's height, gender, family situation, vehicle preference color, intended vehicle price, etc.) of the matching subject to the matching subject (which may be the user with the intention to purchase the vehicle).

[0043] The subject feature type corresponding to the subject feature information may be a matching type of the vehicle information of the vehicle for sale. For example, if the subject feature information is that the color of the vehicle is red, then the corresponding subject feature type is color; if the subject feature information is that the price range of the entire vehicle series is between 100,000 and 200,000, then the corresponding subject feature type is price; for example, if the subject feature information is that the age of the vehicle purchasing population is mostly between 30 and 50 years old, then the corresponding subject feature type is user age.

[0044] like Figure 1 As shown, the information recommendation method described in this application includes the following steps:

[0045] Step S100, obtaining some subject attribute information of the matching subject;

[0046] The subject attribute information of the matching subject is provided by the matching subject, or is obtained according to the vehicle query conditions of the matching subject in the historical period.

[0047] Further, step S100 includes step S110:

[0048] Step S110: Obtain the attribute information of each subject of the matching subject to obtain a subject attribute information list A=(A1, A2, ..., A m ,...,A n ), where m = 1, 2, ..., n, and n is the number of subject attribute information of the matching subject; A m The mth subject attribute information of the matching subject.

[0049] Step S200: Match the subject attribute type corresponding to each subject attribute information with the subject feature type corresponding to each subject feature information, so as to determine the first subject corresponding to each subject attribute information from a plurality of subjects to be confirmed;

[0050] The subject attribute type corresponding to the subject attribute information may be a matching type of the user's personal information. For example, if the subject attribute information is that the user likes red, then the corresponding subject attribute type is color. For another example, if the subject attribute information is that the user's preferred vehicle price is less than RMB 200,000, then the corresponding subject attribute type is price.

[0051] Further, step S200 includes steps S210 to S240:

[0052] Step S210: Obtain the subject attribute type corresponding to each subject attribute information to obtain a subject attribute type list B = (B1, B2, ..., B m ,...,B n ), where B m The subject attribute type corresponding to the mth subject attribute information of the matching subject;

[0053] Step S220: Obtain the subject feature information of each subject to be confirmed, so as to obtain a subject feature information list set C = (C1, C2, ..., C i ,...,C j );C i =(C i1 ,C i2 ,...,C ie ,...,C if(i) ); where i = 1, 2, ..., j; j is the number of entities to be confirmed; C i is the list of subject feature information corresponding to the i-th subject to be confirmed; e=1,2,...,f(i); f(i) is the number of subject feature information of the i-th subject to be confirmed; C ie is the e-th subject feature information of the i-th subject to be confirmed;

[0054] Step S230: Obtain the subject feature type corresponding to each subject feature information to obtain a subject feature type list set D = (D1, D2, ..., D i ,...,D j );D i =(D i1 ,D i2 ,...,D ie ,...,D if(i) ), where D i is the list of subject feature types corresponding to the i-th subject to be confirmed; D ie C ie The corresponding subject feature type;

[0055] Step S240, traverse the list set D of subject feature types, if D ie With B m The same, and C ie With A m If the matching degree of C is greater than the preset matching degree threshold, ie Confirmed as A m The corresponding first feature information, and the i-th subject to be confirmed is determined as A m The corresponding first subject.

[0056] If the subject feature type of any subject feature information of any subject to be confirmed is the same as the subject attribute type of a subject attribute information (such as both are colors), it means that the subject feature information and the subject attribute information are of the same matching type and can be matched (such as the subject attribute information may be that the user prefers the vehicle body to be red, and the subject feature information may be that the vehicle interior is red. If their matching types are not compared and a simple semantic comparison is performed directly, the two may be listed as information with a high degree of matching and output, which will cause errors in information matching). Semantic matching is performed on the subject feature information and the subject attribute information. If the matching degree of the two is greater than a preset matching degree threshold (such as eighty percent), it can be considered that the subject feature information and the subject attribute information are successfully matched, and the subject to be confirmed corresponding to the subject feature information is determined as the first subject corresponding to the subject attribute information.

[0057] The method for determining the first subject is a single match in the information matching step, which only matches a certain subject attribute information and a single subject feature information. In order to further improve the accuracy of information matching, two-stage matching, i.e., combined matching, is required, such as step S300-step S600:

[0058] Step S300: determining subject attribute information whose matching degree with any subject feature information of any first subject is greater than a preset matching degree threshold as first subject attribute information;

[0059] Further, step S300 includes step S310:

[0060] Step S310, traverse each subject attribute information, if B m With D ie The same, and A m With C ie If the matching degree of A is greater than the preset matching degree threshold, m Determined as the first subject attribute information.

[0061] Step S400: determining subject attribute information that does not belong to the first subject attribute information among the plurality of subject attribute information as second subject attribute information;

[0062] Further, step S400 includes step S410:

[0063] Step S410: traverse each subject attribute information, if B m is different from any of the subject feature types in the subject feature type list set D, or A m If the matching degree with each subject feature information in the subject feature information list set C is less than or equal to the preset matching degree threshold, then A mDetermined as the second subject attribute information.

[0064] The second subject attribute information is subject attribute information whose corresponding subject attribute type is different from all subject feature types, or whose matching degree with all subject feature information is less than or equal to a preset matching degree threshold. At this time, in order to further improve the accuracy of information matching, it is necessary to perform two-stage matching on the second subject attribute information.

[0065] Step S500: According to the subject attribute type corresponding to each second subject attribute information, determine from a plurality of subject attribute information the third subject attribute information that is combined with each second subject attribute information and meets a preset information combination rule;

[0066] Further, step S500 includes step S510-step S520:

[0067] Step S510: Obtain the subject attribute type corresponding to each second subject attribute information to obtain a second subject attribute type list E=(E1, E2, ..., E g ,...,E h ), where g = 1, 2, ..., h, and h is the number of second subject attribute information; E g is the subject attribute type corresponding to the g-th second subject attribute information;

[0068] Step S520, traverse the subject attribute type list B, if E g With B m There is a preset combination relationship, and E g With B m If the combination of meets the preset information combination rules, then A m The third subject attribute information corresponding to the g-th second subject attribute information is determined.

[0069] The third subject attribute information is the subject attribute information that is combined and matched with a second subject attribute information (for example, the second subject attribute information is that the user's car usage scenario is in a northern city, and all subject feature information does not have the same matching type or the information matching degree is higher than the matching degree threshold. Therefore, it is necessary to determine the corresponding third subject attribute information at this time. The corresponding third subject attribute information can be that the user's car usage time is daytime or the user's car usage season is winter. There is a correlation between the two, which complies with the preset information combination rules, wherein the combination correlation relationship and the information combination rules are statistically set by the staff based on the query conditions of historical users).

[0070] Step S600: determining a target subject from a plurality of first subjects according to each second subject attribute information and its corresponding third subject attribute information, and outputting a subject identifier and subject characteristic information of the target subject;

[0071] Further, step S600 includes step S610-step S640:

[0072] Step S610: Combine each second subject attribute information with its corresponding third subject attribute information to obtain a plurality of fourth subject attribute information;

[0073] If the second main attribute information is that the user uses the car in a northern city, and the third main attribute information is that the user uses the car in winter, then the fourth main attribute information obtained after combination may be that the user uses the car in a northern winter.

[0074] Step S620: Match each fourth subject attribute information with each subject feature information of each first subject to obtain a matching degree between each fourth subject attribute information and each subject feature information of each first subject;

[0075] Step S630: if the matching degree between any subject characteristic information of the first subject and any fourth subject attribute information is greater than a preset matching degree threshold, the subject characteristic information is determined as the second characteristic information;

[0076] The second characteristic information is subject characteristic information whose matching degree with the fourth subject attribute information is greater than a preset matching degree threshold (eg, the second characteristic information is that the lowest applicable temperature of the vehicle is minus twenty degrees Celsius).

[0077] Step S640: determine a target subject from a plurality of first subjects according to the number of second feature information and the number of first feature information corresponding to the first subjects, and output the subject identification and subject feature information of the target subject.

[0078] Wherein, step S640 includes steps S641 to S643:

[0079] Step S641: De-duplicate the first characteristic information and the second characteristic information corresponding to each first subject to obtain the target characteristic information corresponding to each first subject;

[0080] Step S642: determine the ratio of the amount of target characteristic information corresponding to each first subject to the amount of subject characteristic information corresponding to the first subject as the target value corresponding to the first subject;

[0081] Step S643: determine the first subject whose target value is greater than the preset target threshold as the target subject, and output the subject identification and subject characteristic information of the target subject.

[0082] Among several subjects to be confirmed, the subject whose single matching degree and combined matching degree with the subject attribute information are higher than the preset matching degree threshold and the proportion of subject characteristic information is greater than the target threshold is determined as the target subject, that is, the subject to be confirmed that matches the subject attribute information of the matching subject.

[0083] The information recommendation method of the present invention matches the subject attribute type corresponding to each subject attribute information of the matching subject with the subject feature type corresponding to each subject feature information of a plurality of subjects to be confirmed, determines a first subject corresponding to each subject attribute information from a plurality of subjects to be confirmed, and then determines subject attribute information whose matching degree with any subject feature information of any first subject is greater than a preset matching degree threshold as the first subject attribute information, determines subject attribute information that does not belong to the first subject attribute information among the plurality of subject attribute information as the second subject attribute information, and determines, according to the subject attribute type corresponding to each second subject attribute information, a combination of subject attribute information corresponding to each second subject attribute information from the plurality of subject attribute information. The method further comprises the following steps: first, determining a target subject from among the plurality of first subjects according to each second subject attribute information and its corresponding third subject attribute information, outputting the subject identification and subject characteristic information of the target subject, and determining the first subject that meets the single matching rule by performing a single matching between the subject characteristic information of the plurality of subjects to be confirmed and the subject attribute information, and then performing a two-stage matching between the subject characteristic information of the first subject and a combination of the plurality of second subject attribute information and the third subject attribute information, so as to improve the matching accuracy between the subject characteristic information of the determined target subject and the subject attribute information of the matching subject, so as to push more accurate subject characteristic information to the matching subject.

[0084] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0085] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0086] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0087] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0088] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0089] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0090] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0091] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0092] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0093] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0094] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0095] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter.

[0096] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0097] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0098] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0099] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0100] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0101] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0102] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0103] 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. An information recommendation method, characterized in that: Applied to an information recommendation system, the information recommendation system stores subject identifications and subject feature information of a number of subjects to be confirmed, each subject feature information corresponding to a subject feature type; The method comprises the following steps: Step S100, obtaining some subject attribute information of the matching subject; Step S200: Matching the subject attribute type corresponding to each subject attribute information with the subject feature type corresponding to each subject feature information, so as to determine a first subject corresponding to each subject attribute information from the plurality of subjects to be confirmed; Step S300: determining subject attribute information whose matching degree with any subject feature information of any first subject is greater than a preset matching degree threshold as first subject attribute information; Step S400: determining the subject attribute information that does not belong to the first subject attribute information among the plurality of subject attribute information as the second subject attribute information; Step S500: According to the subject attribute type corresponding to each piece of the second subject attribute information, determine, from a plurality of pieces of the subject attribute information, third subject attribute information that is combined with each piece of the second subject attribute information and meets a preset information combination rule; Step S600: Determine a target subject from among the first subjects according to each of the second subject attribute information and its corresponding third subject attribute information, and output the subject identification and subject characteristic information of the target subject.

2. The method according to claim 1, characterized in that The step S100 includes: Step S110: Obtain the attribute information of each subject of the matching subject to obtain a subject attribute information list A=(A1, A2, ..., A m ,...,A n ); wherein m=1, 2, ..., n; n is the number of subject attribute information of the matching subject; A m It is the mth subject attribute information of the matching subject.

3. The method according to claim 2, characterized in that The step S200 includes: Step S210: Obtain the subject attribute type corresponding to each subject attribute information to obtain a subject attribute type list B = (B1, B2, ..., B m ,...,B n ), where B m is the subject attribute type corresponding to the mth subject attribute information of the matching subject; Step S220: Obtain the subject feature information of each subject to be confirmed, so as to obtain a subject feature information list set C = (C1, C2, ..., C i ,...,C j );C i =(C i1 ,C i2 ,...,C ie ,...,C if(i) ); where i = 1, 2, ..., j; j is the number of the subject to be confirmed; C i is a list of subject characteristic information corresponding to the i-th subject to be confirmed; e=1,2,...,f(i); f(i) is the number of subject characteristic information of the i-th subject to be confirmed; C ie is the e-th subject characteristic information of the i-th subject to be confirmed; Step S230: Obtain the subject feature type corresponding to each subject feature information to obtain a subject feature type list set D = (D1, D2, ..., D i ,...,D j );D i =(D i1 ,D i2 ,...,D ie ,...,D if(i) ), where D i is a list of subject feature types corresponding to the i-th subject to be confirmed; D ie C ie The corresponding subject feature type; Step S240: traverse the subject feature type list set D. If D ie With B m The same, and C ie With A m If the matching degree is greater than the preset matching degree threshold, C ie Confirmed as A m The corresponding first characteristic information, and the i-th subject to be confirmed is determined as A m The corresponding first subject.

4. The method according to claim 3, characterized in that The step S300 includes: Step S310: traverse each subject attribute information, if B m With D ie The same, and A m With C ie If the matching degree of A is greater than the preset matching degree threshold, m Determined as the first subject attribute information.

5. The method according to claim 4, characterized in that The step S400 includes: Step S410: traverse each subject attribute information, if B m is different from any of the subject feature types in the subject feature type list set D, or A m If the matching degree of each subject feature information in the subject feature information list set C is less than or equal to the preset matching degree threshold, then A m Determined as the second subject attribute information.

6. The method according to claim 5, characterized in that The step S500 includes: Step S510: Obtain the subject attribute type corresponding to each of the second subject attribute information to obtain a second subject attribute type list E=(E1, E2, ..., E g ,...,E h ); wherein g = 1, 2, ..., h; h is the number of the second subject attribute information; E g is the subject attribute type corresponding to the g-th second subject attribute information; Step S520: traverse the subject attribute type list B. If E g With B m There is a preset combination relationship, and E g With B m If the combination of meets the preset information combination rules, then A m Determine the third subject attribute information corresponding to the g-th second subject attribute information.

7. The method according to claim 6, characterized in that The step S600 includes: Step S610: Combine each second subject attribute information with its corresponding third subject attribute information to obtain a plurality of fourth subject attribute information; Step S620: Match each of the fourth subject attribute information with each of the subject feature information of each of the first subjects to obtain a matching degree between each of the fourth subject attribute information and each of the subject feature information of each of the first subjects; Step S630: if the matching degree between any subject characteristic information of the first subject and any fourth subject attribute information is greater than a preset matching degree threshold, the subject characteristic information is determined as the second characteristic information; Step S640: determine a target subject from a number of the first subjects according to the number of the second characteristic information and the number of the first characteristic information corresponding to the first subjects, and output the subject identification and subject characteristic information of the target subject.

8. The method according to claim 7, characterized in that The step S640 includes: Step S641: De-duplicate the first characteristic information and the second characteristic information corresponding to each of the first subjects to obtain the target characteristic information corresponding to each of the first subjects; Step S642: determine the ratio of the amount of target characteristic information corresponding to each of the first subjects to the amount of subject characteristic information corresponding to the first subject as the target value corresponding to the first subject; Step S643: determine the first subject whose target value is greater than the preset target threshold as the target subject, and output the subject identification and subject characteristic information of the target subject.

9. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the method as claimed in any one of claims 1 to 8.

10. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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