CN110569439A

CN110569439A - Entity display method, entity display device, storage medium and electronic equipment

Info

Publication number: CN110569439A
Application number: CN201910854987.6A
Authority: CN
Inventors: 徐辉
Original assignee: Rajax Network Technology Co Ltd
Current assignee: Rajax Network Technology Co Ltd
Priority date: 2019-09-10
Filing date: 2019-09-10
Publication date: 2019-12-13

Abstract

the embodiment of the invention discloses an entity display method, an entity display device, a storage medium and electronic equipment. The entity attribute expected to be displayed by the target user is determined through the display query instruction of the target user, the user attribute characteristics of the target user are obtained, the expected consumption parameter corresponding to the current query of the target user is predicted according to the user attribute characteristics of the target user and the display query instruction, at least one entity matched with the display query instruction of the target user is determined as a first entity set according to the expected consumption parameter corresponding to the current query, and therefore the first entity set is displayed. In this embodiment, the presentation query instruction may be a presentation query instruction with a clear intention, or may also be a presentation query instruction without a clear intention, so that the entity presentation method of this embodiment has strong flexibility. Meanwhile, the expected consumption parameters are obtained based on the consumption parameter prediction model, so that the entity display method has higher accuracy.

Description

entity display method, entity display device, storage medium and electronic equipment

Technical Field

The invention relates to the field of data processing, in particular to an entity display method, an entity display device, a storage medium and electronic equipment.

background

with the rapid growth of the internet and the increasing abundance of categories of entities (e.g., commodities), O2O (online to offline) transactions become more convenient. While the variety of entities is becoming rich, it is becoming more and more critical how to show the user entities that are more in line with the user's intent, thereby reducing the time the user spends selecting an entity. The existing entity display or recommendation method usually displays entities to a user according to a user image, so that the user does not need to spend a lot of time to select the entities, but sometimes the displayed entities may not meet the intention of the user, so that the accuracy of entity display is low.

Disclosure of Invention

In view of this, embodiments of the present invention provide an entity display method, an entity display apparatus, a storage medium, and an electronic device, which are used to display an entity that better meets the user's intention for a user, and improve the accuracy of entity display.

In a first aspect, an embodiment of the present invention provides an entity display method, where the method includes:

acquiring a display query instruction of a target user, wherein the display query instruction is used for determining entity attributes which the target user desires to display;

Acquiring user attribute characteristics of the target user, wherein the user attribute characteristics at least partially represent the historical behavior of the target user;

predicting expected consumption parameters corresponding to the current query of the target user according to the user attribute characteristics and the display query instruction based on a consumption parameter prediction model, wherein the consumption parameter prediction model is obtained by pre-training a sample set, and the sample set comprises user attribute characteristics corresponding to at least one query of a plurality of users, historical display query instructions and corresponding historical consumption parameters;

determining a first entity set according to the display query instruction and the expected consumption parameter;

and sending the first entity set to a client corresponding to the target user for displaying.

preferably, the acquiring the user attribute characteristics of the target user includes:

Acquiring a historical entity set corresponding to the target user, wherein the historical entity set is a set formed by entities which generate offers by clicking the target user;

And determining the entity attribute characteristics of each entity in the historical entity set as a part of the user attribute characteristics.

preferably, the determining a first set of entities according to the presentation query instruction and the expected consumption parameter comprises:

Acquiring a first consumption parameter interval corresponding to the expected consumption parameter;

Determining a second entity set according to the first consumption parameter interval, wherein the second entity set comprises a plurality of entities;

And determining the first entity set according to at least one entity matched with the presentation query instruction in the second entity set.

Preferably, the determining the second set of entities according to the first consumption parameter interval comprises:

Determining the second entity set according to a plurality of entities with consumption parameters belonging to the first consumption parameter interval.

Acquiring a third entity set matched with the display query instruction and entity attribute characteristics of each entity in the third entity set, wherein the third entity set comprises a plurality of entities;

Determining a ranking score for each of the entities based on the expected consumption parameters and each of the entity attribute characteristics;

Determining the first set of entities from the ranking score.

Preferably, the determining a ranking score for each of the entities based on the expected consumption parameters and each of the entity attribute characteristics comprises:

acquiring weights corresponding to various characteristics in the entity attribute characteristics;

Determining a first score of each corresponding entity according to each entity attribute feature and the weight corresponding to each feature;

and adjusting the first scores of the entities according to the expected consumption parameters, and determining the ranking scores corresponding to the entities.

preferably, the adjusting the first score of each entity according to the expected consumption parameter, and the determining the ranking score corresponding to each entity includes:

Acquiring a second consumption parameter interval corresponding to the expected consumption parameter;

the first score corresponding to the entity of which the consumption parameter belongs to the second consumption parameter interval is adjusted upwards, and the first score after being adjusted upwards is determined as the sorting score;

and determining the first score corresponding to the entity of which the consumption parameter does not belong to the second consumption parameter interval as the ranking score.

in a second aspect, an embodiment of the present invention provides an entity display apparatus, where the apparatus includes:

The system comprises a first acquisition unit, a second acquisition unit and a display unit, wherein the first acquisition unit is used for acquiring a display query instruction of a target user, and the display query instruction is used for determining entity attributes which the target user desires to display;

A second obtaining unit, configured to obtain a user attribute feature of the target user, where the user attribute feature at least partially represents a historical behavior of the target user;

the prediction unit is used for predicting expected consumption parameters corresponding to the current query of the target user according to the user attribute characteristics and the display query instruction based on a consumption parameter prediction model, the consumption parameter prediction model is obtained by pre-training a sample set, and the sample set comprises user attribute characteristics corresponding to at least one query of a plurality of users, historical display query instructions and corresponding historical consumption parameters;

A determining unit, configured to determine a first entity set according to the presentation query instruction and the expected consumption parameter;

And the sending unit is used for sending the first entity set to a client corresponding to the target user for displaying.

in a third aspect, the present invention provides a computer-readable storage medium on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the method according to any one of the first aspect.

in a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is configured to store one or more computer program instructions, where the one or more computer program instructions are executed by the processor to implement the following steps:

the entity attribute expected to be displayed by the target user is determined through the display query instruction of the target user, the user attribute characteristics of the target user are obtained, the expected consumption parameter corresponding to the current query of the target user is predicted according to the user attribute characteristics of the target user and the display query instruction, at least one entity matched with the display query instruction of the target user is determined as a first entity set according to the expected consumption parameter corresponding to the current query, and therefore the first entity set is displayed. In this embodiment, the presentation query instruction may be a presentation query instruction with a clear intention, or may also be a presentation query instruction without a clear intention, so that the entity presentation method of this embodiment has strong flexibility. Meanwhile, the expected consumption parameters are obtained based on the consumption parameter prediction model, so that the expected consumption parameters obtained through prediction are more accurate, and the entity display method of the embodiment has higher accuracy.

drawings

the above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

FIG. 1 is a flow chart of an entity display method according to a first embodiment of the present invention;

FIG. 2 is a schematic diagram of an entity set according to an embodiment of the present invention;

FIG. 3 is another schematic diagram of a set of entities of an embodiment of the present invention;

FIG. 4 is a schematic view of a physical display apparatus according to a second embodiment of the present invention;

Fig. 5 is a schematic view of an electronic device according to a third embodiment of the present invention.

Detailed Description

The present disclosure is described below based on examples, but the present disclosure is not limited to only these examples. In the following detailed description of the present disclosure, certain specific details are set forth. It will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present disclosure.

further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.

Unless the context clearly requires otherwise, throughout the description, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".

in the description of the present disclosure, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified.

in this embodiment, an entity is taken as an example for description. Those skilled in the art will readily appreciate that other types of entities (e.g., merchants, etc.) are equally applicable to the method of the present embodiment.

with the rapid growth of the internet and the increasing abundance of categories of entities (e.g., commodities), O2O transactions are becoming more convenient. The existing entity display or recommendation method usually displays the entity to the user according to the user image, so that the user does not need to spend a lot of time to select the entity. However, sometimes the displayed entities may not meet the user's intention, for example, the user needs to select a certain type of goods, and the displayed entities have too many numbers or even include a plurality of other types of entities, which makes it impossible for the user to quickly select the entity that best meets his or her own needs, that is, the entity display accuracy is low.

fig. 1 is a flowchart of an entity display method according to a first embodiment of the present invention. As shown in fig. 1, the method of the present embodiment includes the following steps:

and step S100, acquiring a display query instruction of a target user.

in this embodiment, the presentation query instruction is used to determine an entity attribute that the target user desires to present, where the entity attribute may be an entity of a certain predetermined subject activity (e.g., a subject activity of "people's restaurant"), or an entity of a certain predetermined type (e.g., curry), and the present embodiment is not limited thereto.

And step S200, acquiring the user attribute characteristics of the target user.

in this embodiment, the user attribute features at least partially characterize the historical behavior of the target user. Specifically, the user attribute feature of the target user may include a historical consumption parameter, a number of times of generating an offer, an age (segment), a gender, an occupation attribute, a location range where the target user is located, feature information, and a historical entity set corresponding to the current query of the target user, and may also include an entity attribute feature of each entity in the historical entity set. When a user queries at different query moments, the corresponding user attribute characteristics may be different. For example, the user may change the location range due to business trip, etc.; or the display query instruction corresponding to the previous query is used for representing the entity of the target user who wants to query the books, while the display query instruction corresponding to the current query is used for representing the entity of the target user who wants to query the foods, and the user attribute characteristics of the target user at different moments have great possibility of influencing the selection of the target user at different moments. Therefore, the accuracy of the expected consumption parameters can be effectively improved by acquiring the user attribute characteristics corresponding to the current query of the target user. It is easy to understand that the user attribute feature of the target user may also include other features, and this embodiment is not limited.

The historical consumption parameter is used for reflecting the consumption level of the target user, the accuracy of the entity display method can be improved by performing entity display on the target user according to the historical consumption parameter of the target user, the historical consumption parameter can be an average value of the historical consumption amount, a mode of the historical consumption amount, a maximum (small) value of the historical consumption amount, and the like, and the embodiment is not limited.

the number of times the offer is generated, i.e., the number of times the target user purchases the entity, can partially reflect the degree of preference of the target user for each type of entity (or specific entity). For example, the presentation query instruction sent by the target user is used for reflecting the entity that the target user wants to query the 'personal dessert' class. If the target user purchases Murphy more often (e.g., above a first threshold), it may be determined that the target user has a greater preference for Murphy, which may be a sweet category.

the characteristic information is obtained according to the user attribute characteristics of the target user. Generally, users of similar age (or same age group) and/or same gender and/or same occupation and/or same location range may have similar preferences, and thus may also partially reflect the target user's preference for each type of entity (or particular entity). Specifically, the plurality of users may be clustered according to user attribute features of the plurality of users including the target user, so that feature information of the target user may be obtained. The plurality of users including the target user are a plurality of users having the same or close user attribute characteristics. Further, the feature information of the target user may be obtained by clustering a plurality of users including the target user through a classification algorithm, for example, a KNN (K-Nearest Neighbor) algorithm, a decision tree, a supervised classification model such as a neural network, or an unsupervised classification model such as a K-means algorithm. The training mode of the classification model may be an existing training mode, and is not described herein again. It should be noted that the clustering process may be performed before the server determines the corresponding user as the target user, and the server may obtain the existing feature information of the target user according to the terminal identifier or the user identifier of the target user. Furthermore, the characteristic information of the target user can be limited according to the display query instruction of the target user. For example, if the presentation query instruction sent by the target user is used to reflect the entity that the target user wants food, the characteristic information of the target user related to the real object, such as the preference lightness, can be obtained.

The historical entity set is a set of entities that generate an offer with a click of a target user. In this embodiment, if the target user makes one entity shopping, it can be regarded that the target user generates an offer with the entity. The entity attribute characteristics of each entity may include a real-time click-through rate, a shopping rate, a historical consumption parameter, a number of times an offer is generated, and a type of each entity determined from historical behavior data and real-time behavior data of some or all users. The real-time click rate of the entity is used for reflecting the interest degree of the user for the entity, and the higher the click rate is, the higher the interest degree of the user for the entity is. The shopping rate, that is, the ordering conversion rate, is used for reflecting the ratio of the number of users purchasing an entity to the number of users clicking to view the entity. The historical consumption parameter may be an average value of the historical consumption amount corresponding to the entity, a mode of the historical consumption amount, a maximum (small) value of the historical consumption amount, or the like. The number of offers generated is used to reflect the absolute value of the number of entities purchased. The type is used to reflect the attribute of the entity, and may be preset according to actual requirements, for example, the type may be an entertainment type, an office type, or a cuisine, and the embodiment is not particularly limited. It is easy to understand that the entity attribute feature of each entity may also include other features, and this embodiment is not limited.

And step S300, predicting expected consumption parameters corresponding to the current query of the target user according to the user attribute characteristics and the displayed query instruction based on the consumption parameter prediction model.

specifically, the user attribute characteristics and the display query instruction of the target user can be used as the input of the consumption parameter prediction model, so that the expected consumption parameters corresponding to the current query of the target user can be predicted more accurately. The consumption parameter prediction model is obtained by pre-training according to a sample set, wherein the sample set can comprise user attribute features, historical display query instructions and corresponding historical consumption parameters, and the user attribute features, the historical display query instructions and the corresponding historical consumption parameters correspond to at least one query of a plurality of users. The user attribute characteristics of a user may change with the development of time, and therefore, when any user corresponds to multiple queries, the user attribute characteristics corresponding to each query of the user need to be acquired.

it is easy to understand that if the user attribute features of the target user and the users in the sample set include numerical features, such as entity scores and sales volumes, the features can be directly obtained to be subsequently used as the input of the consumption parameter prediction model; if the user attribute features of the target user and the users in the sample set include non-numerical features, for example, entity categories, the corresponding relationship between each feature and a predetermined numerical value may be predetermined, for example, the category is sichuan dish corresponding to 1, yue dish corresponding to 2, and the like, so that the non-numerical features may be converted into numerical features as input of the consumption parameter prediction model according to the corresponding relationship between the non-numerical features and the predetermined numerical values in the following.

in this embodiment, the initial model corresponding to the consumption parameter prediction model may be a tree model, a bayesian classifier, a neural network, or the like, which is not limited in this embodiment. Taking a Neural Network as an example, the Neural Network is called an Artificial Neural Network (ANN) and is an information processing model formed by interconnecting a large number of processing units. Common artificial Neural networks include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and the like. The ANN has the characteristics of nonlinearity (suitable for processing nonlinear information), non-limitation (namely, the overall behavior of a system depends on the interaction between processing units), extraordinary qualitative (namely, self-adaptation, self-organization and self-learning capabilities, and can continuously perform self-learning in the process of processing information) and non-convexity (the activation function of the model has a plurality of extreme values, so that the model has a plurality of stable equilibrium states, and the change of the model is various), and therefore, the ANN can be widely applied to various fields to perform more accurate data prediction.

In the training process of the consumption parameter prediction model, the user attribute characteristics and the historical display query instructions corresponding to the queries of the users in the sample set are respectively used as input, and the corresponding historical consumption parameters are used as output to train the initial model, so that the consumption parameter prediction model can be obtained.

further, in order to improve the accuracy of the consumption parameter prediction model, the sample set may be further randomly divided into a training sample subset and a testing sample subset before training, and the consumption parameter prediction model is trained through the training sample subset and tested through the testing sample subset until the accuracy of the consumption parameter prediction model reaches an expected value.

step S400, a first entity set is determined according to the display query instruction and the expected consumption parameter.

In an optional implementation manner of this embodiment, a first consumption parameter interval corresponding to an expected consumption parameter may be obtained, and a second entity set is determined according to the first consumption interval, so that the first entity set is determined according to at least one entity, which is matched with the presentation query instruction, in the second entity set.

specifically, a corresponding relationship between the expected consumption parameter and a first consumption parameter interval may be obtained in advance, for example, the expected consumption parameter is 55, and the corresponding first consumption interval is [60,70 ]; the expected consumption parameter is 70, the corresponding first consumption interval is [70,80], and the like, so that the first consumption parameter interval corresponding to the current query of the target user can be obtained according to the corresponding relation between the expected consumption parameter and the first consumption parameter interval. The second set of entities may be determined from a plurality of entities for which a consumption parameter belongs to the first consumption parameter interval.

fig. 2 is a schematic diagram of an entity set according to an embodiment of the present invention. It is to be understood that the number of entities in the entity set and the entity attribute characteristics corresponding to each entity shown in fig. 2 are merely illustrative. The set of entities shown in FIG. 2 is an initial set of entities, which includes items 1-7. The expected consumption parameter corresponding to the current query of the target user is 20 yuan, the corresponding first consumption parameter interval is 25-35 yuan, and the second entity set is composed of a commodity 1, a commodity 3, a commodity 4 and a commodity 5. If the display query instruction of the target user for the current query is a display query instruction of a Chinese food, the entities in the second entity set that are matched with the display query instruction of the target user for the current query are a commodity 3 and a commodity 5, that is, the first entity set includes the commodity 3 and the commodity 5.

in another optional implementation manner of this embodiment, the third entity set matched with the presentation query instruction and the entity attribute features of the entities in the third entity set may be obtained, and the ranking score of each entity is determined according to the expected consumption parameter and the entity attribute features of each entity, so that the first entity set is determined according to the ranking score.

Specifically, in the process of determining the ranking score of each entity in the third entity set, weights corresponding to each feature in the entity attribute features of each entity may be obtained, and the first score of each corresponding entity is determined according to the entity attribute features of each entity and the weights corresponding to each feature, so that the first score of each entity is adjusted according to the expected consumption parameters, and the ranking score corresponding to each entity is determined. The first score of each entity is the weighted sum of the entity attribute characteristics of each entity and the weight corresponding to each characteristic. It will be readily appreciated that in calculating the first score for each entity, the entity attribute characteristics for each entity do not include the consumption parameters for each entity.

in determining the first set of entities, an entity may be added to the first set of entities when its ranking score satisfies a predetermined condition. Alternatively, the predetermined condition may be set according to actual requirements, for example, the ranking score is in the top n maximum bits, or the ranking score is greater than the second threshold and is in the top n maximum bits, and the like. Wherein n is a predetermined integer greater than or equal to 1.

It is easy to understand that if the entity attribute characteristics of the entity include numerical characteristics, the entity attribute characteristics can be directly used for calculating the first score of the entity in the following according to the characteristics; if the entity attribute features of the entity include non-numerical features, the corresponding relationship between each non-numerical feature and a predetermined numerical value can be predetermined, so that the non-numerical features can be converted into numerical features according to the corresponding relationship between the non-numerical features and the predetermined numerical values in the subsequent process, and the numerical features can be used for calculating the first score of the entity in the subsequent process.

more specifically, in the process of adjusting the first score of each entity according to the expected consumption parameter, a second consumption parameter interval corresponding to the expected consumption parameter may be obtained, and for an entity whose consumption parameter belongs to the second consumption parameter interval, the first score corresponding to the entity may be adjusted upward, so that the adjusted first score is determined as the ranking score of the entity; for an entity with a consumption parameter not belonging to the second consumption parameter interval, a first score corresponding to the entity may be determined as a ranking score. It is easy to understand that the first consumption parameter interval and the second consumption parameter interval may be the same or different.

for example, the target user queries this time with the corresponding expected consumption parameter of 55 and the corresponding second consumption parameter interval of [50,100 ]. The third entity set comprises an entity 1 and an entity 2, the consumption parameter corresponding to the entity 1 is 83, and the first score is 55; entity 2 corresponds to a consumption parameter of 49 and a first score of 57. For entity 1, the corresponding first score of entity 1 may be adjusted up, for example, by 10 points, or multiplied by 1.5 (i.e., a predetermined coefficient greater than 1), so as to determine the adjusted up first score as the ranking score of entity 1; for entity 2, a first score 57 corresponding to entity 2 may be determined as the ranking score for entity 2.

FIG. 3 is another schematic diagram of an entity set according to an embodiment of the invention. It is to be understood that the number of entities in the entity set and the entity attribute characteristics corresponding to each entity shown in fig. 3 are merely illustrative. The entity set shown in fig. 3 is a third entity set matched with the presentation query instruction queried by the target user this time, and the third entity set includes commodities 1 to 7. The expected consumption parameter corresponding to the current query of the target user is 20 yuan, the corresponding first consumption parameter interval is 25-35 yuan, the first score is adjusted up by 10 minutes, and the preset condition is that the ranking score is not less than 75 minutes and the ranking is at the top 4 maximum. Then item 3, item 4, item 5, and item 7 are included in the first set of entities.

and step S500, sending the first entity set to a client corresponding to the target user for displaying.

In an optional implementation manner of this embodiment, the server may send the first entity set to a client of the target user, so that the client may sort the entities according to any one of the entity attribute features of each entity in the first entity set, for example, sort the entities according to entity scores, sales volumes, and the like, so as to display the entities to the target user, and thus, the target user may view the entities that are more in line with the intention of the target user.

In another optional implementation manner of this embodiment, the first entity set includes entities whose consumption parameters do not belong to the second consumption parameter interval, so that the server may send the first entity set to the client of the target user, so that the client may preferentially display the entities whose consumption parameters belong to the second consumption parameter interval, and thus, the target user may preferentially view the entities that better meet the intention of the target user.

In this embodiment, the entity attribute that the target user desires to display is determined through the display query instruction of the target user, and the user attribute feature of the target user is obtained, so as to predict the expected consumption parameter corresponding to the current query of the target user according to the user attribute feature of the target user and the display query instruction, and determine at least one entity matched with the display query instruction of the target user as a first entity set according to the expected consumption parameter corresponding to the current query, thereby displaying the first entity set. In this embodiment, the presentation query instruction may be a presentation query instruction with a clear intention, or may also be a presentation query instruction without a clear intention, so that the entity presentation method of this embodiment has strong flexibility. Meanwhile, the expected consumption parameters are obtained based on the consumption parameter prediction model, so that the expected consumption parameters obtained through prediction are more accurate, and the entity display method of the embodiment has higher accuracy.

Fig. 4 is a schematic diagram of a physical display apparatus according to a second embodiment of the invention. As shown in fig. 4, the apparatus of the present embodiment includes a first acquisition unit 41, a second acquisition unit 42, a prediction unit 43, a determination unit 44, and a transmission unit 45.

The first obtaining unit 41 is configured to obtain a presentation query instruction of a target user, where the presentation query instruction is used to determine an entity attribute that the target user desires to present. The second obtaining unit 42 is configured to obtain a user attribute feature of the target user, where the user attribute feature at least partially characterizes a historical behavior of the target user. The prediction unit 43 is configured to predict, based on a consumption parameter prediction model, an expected consumption parameter corresponding to the current query of the target user according to the user attribute features and the display query instruction, where the consumption parameter prediction model is obtained by pre-training a sample set, where the sample set includes user attribute features, historical display query instructions, and corresponding historical consumption parameters, and the user attribute features correspond to at least one query of multiple users. The determining unit 44 is configured to determine the first entity set according to the presentation query instruction and the expected consumption parameter. The sending unit 45 is configured to send the first entity set to a client corresponding to the target user for displaying.

further, the second obtaining unit 42 comprises a first obtaining subunit 421 and a first determining subunit 422.

The first obtaining subunit 421 is configured to obtain a history entity set corresponding to the target user, where the history entity set is a set formed by entities that generate an offer with a click of the target user. The first determining subunit 422 is configured to determine an entity attribute feature of each entity in the historical entity set as a part of the user attribute feature.

further, the determination unit 44 includes a second acquisition subunit 441, a second determination subunit 442, and a third determination subunit 443.

the second obtaining subunit 441 is configured to obtain a first consumption parameter interval corresponding to the expected consumption parameter. The second determining subunit 442 is configured to determine a second entity set according to the first consumption parameter interval, where the second entity set includes a plurality of entities. The third determining subunit 443 is configured to determine the first entity set according to at least one entity in the second entity set that matches the presentation query instruction.

further, the second determining subunit 442 is configured to determine the second set of entities according to a plurality of entities whose consumption parameters belong to the first consumption parameter interval.

Further, the determining unit 44 includes a third obtaining sub-unit 444, a fourth determining sub-unit 445 and a fifth determining sub-unit 446.

the third obtaining subunit 444 is configured to obtain a third entity set matched with the presentation query instruction and an entity attribute feature of each entity in the third entity set, where the third entity set includes multiple entities. The fourth determining subunit 445 is configured to determine a ranking score of each of the entities according to the expected consumption parameter and each of the entity attribute characteristics. A fifth determining subunit 446 is configured to determine the first set of entities according to the ranking score.

Further, the fourth determining subunit 445 includes a first obtaining module, a second determining module, and a third determining module.

The first obtaining module is used for obtaining the weight corresponding to each feature in the entity attribute features. The second determining module is used for determining the first scores of the corresponding entities according to the attribute characteristics of the entities and the weights corresponding to the characteristics. The third determining module is configured to adjust the first score of each entity according to the expected consumption parameter, and determine the ranking score corresponding to each entity.

further, the third determining module comprises an obtaining sub-module, a first determining sub-module and a second determining sub-module.

the obtaining submodule is used for obtaining a second consumption parameter interval corresponding to the expected consumption parameter. The first determining submodule is used for adjusting the first score corresponding to the entity of which the consumption parameter belongs to the second consumption parameter interval upwards, and determining the first score after the adjustment upwards as the ranking score. The second determining submodule is used for determining the first score corresponding to the entity of which the consumption parameter does not belong to the second consumption parameter interval as the ranking score.

Further, the fifth determining subunit 446 is configured to determine the first set of entities from at least one of the entities for which a consumption parameter fulfils a predetermined condition.

fig. 5 is a schematic view of an electronic device according to a third embodiment of the present invention. As shown in fig. 5, the electronic device: at least one processor 501; and a memory 502 communicatively coupled to the at least one processor 501; and a communication component 503 in communicative connection with the scanning device, the communication component 503 receiving and transmitting data under the control of the processor 501; wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions being executable by the at least one processor 501 to implement:

Further, the obtaining of the user attribute characteristics of the target user includes:

Further, the determining a first set of entities from the presentation query instruction and the expected consumption parameter comprises:

further, the determining a second set of entities according to the first consumption parameter interval includes:

determining the first set of entities from the ranking score.

Further, the determining a ranking score for each of the entities based on the expected consumption parameter and each of the entity attribute features comprises:

further, the adjusting the first score of each entity according to the expected consumption parameter, and determining the ranking score corresponding to each entity includes:

Further, the determining the first set of entities from the ranking score comprises:

determining the first set of entities from at least one of the entities for which a consumption parameter meets a predetermined condition.

specifically, the electronic device includes: one or more processors 501 and a memory 502, with one processor 501 being an example in fig. 5. The processor 501 and the memory 502 may be connected by a bus or other means, and fig. 5 illustrates the connection by the bus as an example. Memory 502, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 501 executes various functional applications and data processing of the device by executing nonvolatile software programs, instructions and modules stored in the memory 502, so as to realize the entity exposure method.

the memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 502 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to an external device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

One or more modules are stored in the memory 502, and when executed by the one or more processors 501, perform the entity exposure method of any of the method embodiments described above.

The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.

a fourth embodiment of the invention relates to a non-volatile storage medium for storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.

that is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The embodiment of the invention discloses A1 and an entity display method, wherein the method comprises the following steps:

a2, the method of A1, wherein the obtaining the user attribute characteristics of the target user comprises:

A3, the method of A1, wherein the determining a first set of entities based on the presentation query instructions and the expected consumption parameters comprises:

a4, the method as in A3, wherein the determining a second set of entities from the first consumption parameter interval comprises:

a5, the method of A1, wherein the determining a first set of entities based on the presentation query instructions and the expected consumption parameters comprises:

determining the first set of entities from the ranking score.

a6, the method of claim 5, wherein the determining a ranking score for each of the entities based on the expected consumption parameters and each of the entity attribute characteristics includes:

a7, the method as recited in a6, wherein the adjusting the first score of each of the entities according to the expected consumption parameter includes:

a8, the method of A5, wherein the determining the first set of entities according to the ranking score comprises:

the embodiment of the invention also discloses B1 and an entity display device, wherein the device comprises:

b2, the device as B1, the second obtaining unit includes:

A first obtaining subunit, configured to obtain a history entity set corresponding to the target user, where the history entity set is a set formed by entities that generate an offer by clicking the target user;

A first determining subunit, configured to determine an entity attribute feature of each entity in the historical entity set as a part of the user attribute feature.

b3, the device as B1, the determining unit includes:

the second acquisition subunit is used for acquiring a first consumption parameter interval corresponding to the expected consumption parameter;

A second determining subunit, configured to determine a second entity set according to the first consumption parameter interval, where the second entity set includes multiple entities;

A third determining subunit, configured to determine the first entity set according to at least one entity in the second entity set that matches the presentation query instruction.

b4, the apparatus as described in B3, the second determining subunit being configured to determine the second set of entities from a plurality of entities whose consumption parameters belong to the first consumption parameter interval.

b5, the device as B1, the determining unit includes:

a third obtaining subunit, configured to obtain a third entity set that is matched with the presentation query instruction and an entity attribute feature of each entity in the third entity set, where the third entity set includes multiple entities;

a fourth determining subunit, configured to determine a ranking score of each entity according to the expected consumption parameter and each entity attribute feature;

a fifth determining subunit, configured to determine the first entity set according to the ranking score.

B6, the apparatus as described in B5, wherein the fourth determining subunit comprises:

the first acquisition module is used for acquiring weights corresponding to various characteristics in the entity attribute characteristics;

A second determining module, configured to determine a first score of each corresponding entity according to each attribute feature of the entity and the weight corresponding to each feature;

and the third determining module is used for adjusting the first scores of the entities according to the expected consumption parameters and determining the ranking scores corresponding to the entities.

B7, the apparatus as described in B6, the third determining module comprising:

The obtaining submodule is used for obtaining a second consumption parameter interval corresponding to the expected consumption parameter;

the first determining submodule is used for adjusting the first score corresponding to the entity of which the consumption parameter belongs to the second consumption parameter interval upwards and determining the first score after being adjusted upwards as the sorting score;

A second determining submodule, configured to determine, as the ranking score, the first score corresponding to the entity whose consumption parameter does not belong to the second consumption parameter interval.

B8, the apparatus as described in B5, the fifth determining subunit is configured to determine the first set of entities according to at least one of the entities whose consumption parameter satisfies a predetermined condition.

the embodiment of the invention also discloses C1, a computer readable storage medium, wherein the computer program instructions are stored on the computer readable storage medium, and when the computer program instructions are executed by a processor, the method of any one of A1-A8 is realized.

the embodiment of the invention also discloses D1, an electronic device, comprising a memory and a processor, wherein the memory is used for storing one or more computer program instructions, and the one or more computer program instructions are executed by the processor to realize the following steps:

D2, the electronic device as recited in D1, wherein the obtaining the user attribute characteristics of the target user comprises:

D3, the electronic device as recited in D1, the determining a first set of entities from the presentation query instruction and the expected consumption parameter comprising:

D4, the electronic device as recited in D3, wherein said determining a second set of entities from the first consumption parameter interval comprises:

D5, the electronic device as recited in D1, the determining a first set of entities from the presentation query instruction and the expected consumption parameter comprising:

determining the first set of entities from the ranking score.

D6, the electronic device of D5, wherein the determining a ranking score for each of the entities based on the expected consumption parameters and each of the entity attribute features comprises:

d7, the electronic device of D6, wherein the adjusting the first score of each of the entities according to the expected consumption parameter includes:

D8, the electronic device of D5, the determining the first set of entities from the ranking scores comprising:

it will be understood by those of ordinary skill in the art that the foregoing embodiments are specific embodiments for practicing the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.

Claims

1. An entity display method, the method comprising:

2. the method of claim 1, wherein the obtaining the user attribute characteristics of the target user comprises:

3. The method of claim 1, wherein determining the first set of entities based on the presentation query and the expected consumption parameters comprises:

4. the method of claim 3, wherein the determining the second set of entities according to the first consumption parameter interval comprises:

5. The method of claim 1, wherein determining the first set of entities based on the presentation query and the expected consumption parameters comprises:

determining the first set of entities from the ranking score.

6. The method of claim 5, wherein determining a ranking score for each of the entities based on the expected consumption parameter and each of the entity attribute features comprises:

7. The method of claim 6, wherein the adjusting the first score for each of the entities according to the expected consumption parameter comprises:

8. An entity display apparatus, the apparatus comprising:

9. a computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-7.

10. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to perform the steps of: