CN111199459A - Commodity recommendation method and device, electronic equipment and storage medium - Google Patents

Commodity recommendation method and device, electronic equipment and storage medium Download PDF

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CN111199459A
CN111199459A CN201911394011.1A CN201911394011A CN111199459A CN 111199459 A CN111199459 A CN 111199459A CN 201911394011 A CN201911394011 A CN 201911394011A CN 111199459 A CN111199459 A CN 111199459A
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CN111199459B (en
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赵豫陕
张军涛
肖淋峰
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Shenzhen Mengtian Technology Co ltd
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Abstract

The embodiment of the disclosure discloses a commodity recommendation method, a commodity recommendation device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring a knowledge graph according to the incidence relation between users and the interaction records of the users and the commodities; grouping the plurality of users based on the knowledge graph to obtain a plurality of user groups; inputting any user group vector and any vector of the grouped sales promotion commodities into a pre-trained commodity purchase prediction model to obtain the probability of the user group for purchasing the grouped sales promotion commodities; and determining a strategy for recommending the grouped sales promotion commodities to the user groups according to the probability of purchasing the grouped sales promotion commodities by the user groups, and recommending the grouped sales promotion commodities to the user groups according to the strategy. The technical scheme of the embodiment of the disclosure can recommend suitable grouping promotion commodities to each user group, can realize personalized recommendation, and can improve the grouping success rate of grouping promotion commodities.

Description

Commodity recommendation method and device, electronic equipment and storage medium
Technical Field
The embodiment of the disclosure relates to the technical field of computer application, in particular to a commodity recommendation method and device, electronic equipment and a storage medium.
Background
In order to reduce the inventory of goods and enhance the user's viscosity, the current e-commerce platforms generally adopt a way of grouping together for promotion to be uniformly displayed to users, namely: after the commodities to be promoted by grouping are extracted, the commodities are uniformly displayed to users in a designated module, and the grouped commodities seen by all the users are the same.
The E-commerce adopts a group-piecing sales promotion mode, which not only reduces the commodity inventory, but also improves the user activity and promotes the lasting development of the platform. However, the current grouping promotion mode of each platform has a great defect, specifically, when facing different users, grouping promotion commodities displayed to all users are the same, and individuation of the grouping promotion commodities is not achieved, so that the conversion rate of the grouping promotion commodities is not high enough.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method and an apparatus for recommending commodities, an electronic device, and a storage medium, so as to implement personalized recommendation and improve the grouping success rate of grouping and promoting commodities.
Additional features and advantages of the disclosed embodiments will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosed embodiments.
In a first aspect, an embodiment of the present disclosure provides a commodity recommendation method, including:
acquiring a knowledge graph according to the incidence relation between users and the interaction records of the users and the commodities, wherein the knowledge graph comprises a plurality of users and the incidence relation between the users;
grouping the plurality of users based on the knowledge graph to obtain a plurality of user groups;
inputting any user group vector and any vector of the grouped sales promotion commodities into a pre-trained commodity purchase prediction model to obtain the probability of the user group for purchasing the grouped sales promotion commodities;
and determining a strategy for recommending the grouped sales promotion commodities to the user groups according to the probability of purchasing the grouped sales promotion commodities by the user groups, and recommending the grouped sales promotion commodities to the user groups according to the strategy.
In one embodiment, obtaining the knowledge graph according to the association relationship between the users and the interaction records of the users and the commodities includes:
generating a first knowledge graph according to the incidence relation among users and the interaction records of the users and the commodities, wherein the first knowledge graph comprises a plurality of commodities, a plurality of users and the incidence relation among the commodities and the users;
and generating the knowledge graph according to the relation between users in the first knowledge graph.
In an embodiment, the clustering the plurality of users based on the knowledge-graph to obtain a plurality of user groups comprises:
randomly selecting a first number of users from the knowledge-graph as a central user group;
regarding any central user in the central user group, taking the central user and the user associated with the central user as a user group;
if any user does not belong to any user group, the user is added into the central user group, if any user belongs to a plurality of user groups, the user is randomly added into any user group, and the user is removed from other user groups.
In one embodiment, determining a policy for recommending the grouped promotional items to the user groups according to the probability of each group purchasing each group of users, recommending the grouped promotional items to the user groups according to the policy includes:
and for any user group, selecting the grouped sales promotion commodities with the highest probability from the grouped sales promotion commodities, and recommending the grouped sales promotion commodities to all users of the user group.
In one embodiment, the user group vector is an average vector of vectors of users included in the user group.
In one embodiment, the interaction record comprises one or more of a purchase record, a collection record, a click record, an evaluation record, and a return record;
the association relationship between the users comprises one or more of an invitation relationship and a geographical relationship, wherein the geographical relationship comprises a native place and/or a residential place.
In one embodiment, the commodity purchase prediction model is obtained by training:
acquiring a training sample set, wherein the training sample comprises a user group vector, a commodity vector and a label for representing the probability of purchasing the commodity by the user group;
determining an initialized commodity purchase prediction model, wherein the initialized commodity purchase prediction model comprises a target layer for outputting a probability of a user group purchasing a commodity;
and training to obtain the commodity purchase prediction model by using a machine learning method, wherein the user group vector and the commodity vector in the training samples in the training sample set are used as the input of the initialized commodity purchase prediction model, and the label corresponding to the input user group vector and the commodity vector is used as the output of the initialized commodity purchase prediction model.
In one embodiment, the initialized goods purchase prediction model comprises an input layer, a first hidden layer, a second hidden layer, a softmax layer, and an output layer, wherein the first hidden layer and the second hidden layer adopt a ReLU function as an activation function.
In a second aspect, an embodiment of the present disclosure further provides a commodity recommendation device, including:
the system comprises a knowledge graph acquisition unit, a commodity interaction unit and a commodity interaction unit, wherein the knowledge graph acquisition unit is used for acquiring a knowledge graph according to the association relationship among users and the interaction records of the users and commodities, and the knowledge graph comprises a plurality of users and the association relationship among the users;
the user clustering unit is used for clustering the plurality of users based on the knowledge graph to obtain a plurality of user groups;
the probability prediction unit is used for inputting any user group vector and any vector of the grouped sales promotion commodities into a commodity purchasing prediction model trained in advance to obtain the probability of the user group for purchasing the grouped sales promotion commodities;
and the strategy determining and commodity recommending unit is used for determining a strategy for recommending the plurality of grouping promotion commodities to the plurality of user groups according to the probability of purchasing each grouping promotion commodity by each user group, and recommending the plurality of grouping promotion commodities to the plurality of user groups according to the strategy.
In one embodiment, the knowledge-graph obtaining unit is configured to:
generating a first knowledge graph according to the incidence relation among users and the interaction records of the users and the commodities, wherein the first knowledge graph comprises a plurality of commodities, a plurality of users and the incidence relation among the commodities and the users;
and generating the knowledge graph according to the relation between users in the first knowledge graph.
In one embodiment, the user clustering unit is configured to:
randomly selecting a first number of users from the knowledge-graph as a central user group;
regarding any central user in the central user group, taking the central user and the user associated with the central user as a user group;
if any user does not belong to any user group, the user is added into the central user group, if any user belongs to a plurality of user groups, the user is randomly added into any user group, and the user is removed from other user groups.
In one embodiment, the policy determination and goods recommendation unit is configured to:
and for any user group, selecting the grouped sales promotion commodities with the highest probability from the grouped sales promotion commodities, and recommending the grouped sales promotion commodities to all users of the user group.
In one embodiment, the user group vector is an average vector of vectors of users included in the user group.
In one embodiment, the interaction record comprises one or more of a purchase record, a collection record, a click record, an evaluation record, and a return record;
the association relationship between the users comprises one or more of an invitation relationship and a geographical relationship, wherein the geographical relationship comprises a native place and/or a residential place.
In one embodiment, the commodity purchase prediction model is obtained by training the following modules:
the system comprises a sample acquisition module, a training sample collection and a training sample collection, wherein the training sample collection comprises a user group vector, a commodity vector and a label for representing the probability of purchasing the commodity by the user group;
a model determination module to determine an initialized merchandise purchase prediction model, wherein the initialized merchandise purchase prediction model includes a target layer to output a probability that a group of users purchased merchandise;
and the model training module is used for training to obtain the commodity purchase prediction model by using a machine learning device and taking the user group vector and the commodity vector in the training samples in the training sample set as the input of the initialized commodity purchase prediction model and taking the label corresponding to the input user group vector and the commodity vector as the output of the initialized commodity purchase prediction model.
In one embodiment, the initialized goods purchase prediction model comprises an input layer, a first hidden layer, a second hidden layer, a softmax layer, and an output layer, wherein the first hidden layer and the second hidden layer adopt a ReLU function as an activation function.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the instructions of the method of any one of the first aspects.
In a fourth aspect, the disclosed embodiments also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method according to any one of the first aspect.
The method and the device for recommending the grouped sales promotion commodities have the advantages that the knowledge map is obtained according to the incidence relation among users and the interaction records of the users and the commodities, the users are grouped based on the knowledge map to obtain a plurality of user groups, any user group vector and any vector of the grouped sales promotion commodities are input into a pre-trained commodity purchase prediction model to obtain the probability of purchasing the grouped sales promotion commodities by the user groups, the strategy of recommending the grouped sales promotion commodities to the user groups is determined according to the probability of purchasing the grouped sales promotion commodities by the user groups, the grouped sales promotion commodities are recommended to the user groups according to the strategy, the suitable grouped sales promotion commodities can be recommended to the user groups, personalized recommendation can be achieved, and the grouped sales promotion success rate of the grouped sales promotion commodities can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments of the present disclosure will be briefly described below, and it is obvious that the drawings in the following description are only a part of the embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to the contents of the embodiments of the present disclosure and the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a commodity recommendation method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram illustrating a training method of a merchandise purchase prediction model according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of another commodity recommendation method provided in the embodiments of the present disclosure;
FIG. 4 is a partial content of a first example knowledge-graph provided by an embodiment of the present disclosure;
FIG. 5 is an example of a knowledge-graph generated from FIG. 4 provided by an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a commodity recommending device according to an embodiment of the present disclosure;
FIG. 7 is a schematic structural diagram of a training module of a merchandise purchase prediction model provided by an embodiment of the present disclosure;
FIG. 8 shows a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
In order to make the technical problems solved, technical solutions adopted and technical effects achieved by the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be described in further detail below with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments, but not all embodiments, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.
It should be noted that the terms "system" and "network" are often used interchangeably in the embodiments of the present disclosure. Reference to "and/or" in embodiments of the present disclosure is meant to include any and all combinations of one or more of the associated listed items. The terms "first", "second", and the like in the description and claims of the present disclosure and in the drawings are used for distinguishing between different objects and not for limiting a particular order.
It should also be noted that, in the embodiments of the present disclosure, each of the following embodiments may be executed alone, or may be executed in combination with each other, and the embodiments of the present disclosure are not limited specifically.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The technical solutions of the embodiments of the present disclosure are further described by the following detailed description in conjunction with the accompanying drawings.
Fig. 1 shows a flowchart of a product recommendation method provided in an embodiment of the present disclosure, where this embodiment is applicable to a case where a group-together sales promotion product is recommended to a user group, and the method may be executed by a product recommendation device configured in an electronic device, as shown in fig. 1, the product recommendation method according to this embodiment includes:
in step S110, a knowledge graph is obtained according to the association relationship between users and the interaction records of the users and the commodities, where the knowledge graph includes a plurality of users and the association relationship between the users.
Wherein the interaction record comprises a plurality of records, such as one or more of a purchase record, a collection record, a click record, an evaluation record, and a return record.
Wherein the association relationship between the users comprises one or more of various relationships, such as invitation relationship and regional (such as native place, residence place, etc.) relationship.
This step may be implemented in various ways, for example, a first knowledge graph may be generated according to the association relationship between users and the interaction records between users and commodities, where the first knowledge graph includes commodities, users, and the association relationship between the commodities and the users. And generating the knowledge graph according to the relation between users in the first knowledge graph.
In step S120, the users are grouped based on the knowledge graph to obtain a plurality of user groups.
For example, a first number of users may be randomly selected from the knowledge-graph as a central user group; and regarding any central user in the central user group, taking the central user and the user associated with the central user as a user group. For the above grouping, if any user does not belong to any user group, the user can be added into the central user group, and if any user belongs to a plurality of user groups, the user can be randomly added into any user group, and the user is removed from other user groups.
In step S130, a vector of any group of users and a vector of any piece of grouped promotional merchandise are input into a pre-trained merchandise purchase prediction model, and a probability that the group of users purchases the piece of grouped promotional merchandise is obtained.
The commodity purchase prediction model can determine the probability value of the vector of the user group purchase input grouping promotion commodity corresponding to the input user/user group vector, and can be obtained through various training modes, as long as the commodity purchase prediction model has the functions, and the specific training mode is not limited by the embodiment.
For example, fig. 2 is a flowchart illustrating a training method of a commodity purchase prediction model provided in an embodiment of the present disclosure, where the commodity purchase prediction model can be obtained by training through the steps shown in fig. 2:
in step S210, a training sample set is obtained, where the training sample includes a user group vector, a commodity vector, and a label representing a probability that the user group purchased the commodity.
The user group vector may be a vector corresponding to one user, or may be a vector corresponding to one user group.
For example, the grouped sales promotion commodities clicked by all users in each user group are defined as a positive sample of the user group, and the positive sample is marked as 1; randomly selecting a part of the group-pieced sales promotion commodities which are not clicked by each user group as a negative sample of the user group, and marking the negative sample as 0.
In step S220, an initialized merchandise purchase prediction model is determined, wherein the initialized merchandise purchase prediction model includes a target layer for outputting a probability that a user group purchases merchandise.
In step S230, the user group vector and the commodity vector in the training samples in the training sample set are input as an initialized commodity purchase prediction model by using a machine learning method, and a label corresponding to the input user group vector and the commodity vector is output as an initialized commodity purchase prediction model, and the commodity purchase prediction model is obtained by training.
For example, the initialized goods purchase prediction model may include an input layer, a first hidden layer, a second hidden layer, a softmax layer, and an output layer, and the first hidden layer and the second hidden layer employ a ReLU function as an activation function.
In step S140, a policy for recommending the grouped promotional items to the user groups is determined according to the probability of purchasing each grouped promotional item by each user group, and the grouped promotional items are recommended to the user groups according to the policy.
For example, for any user group, the piece-together promotion item with the highest probability can be selected from the piece-together promotion items, and recommended to all users of the user group.
The vector of the user included in any user group can be averaged to be used as the user group vector.
Acquiring a knowledge graph according to the incidence relation between users and the interaction records of the users and the commodities, wherein the knowledge graph comprises a plurality of users and the incidence relation between the users;
grouping the plurality of users based on the knowledge graph to obtain a plurality of user groups;
inputting any user group vector and any vector of the grouped sales promotion commodities into a pre-trained commodity purchase prediction model to obtain the probability of the user group for purchasing the grouped sales promotion commodities;
and determining a strategy for recommending the grouped sales promotion commodities to the user groups according to the probability of purchasing the grouped sales promotion commodities by the user groups, and recommending the grouped sales promotion commodities to the user groups according to the strategy.
The method includes the steps of obtaining a knowledge graph according to incidence relations among users and interaction records of the users and commodities, grouping the users to obtain a plurality of user groups based on the knowledge graph, inputting any user group vector and any vector of the grouped promotional commodities to a pre-trained commodity purchasing prediction model to obtain the probability of the user groups purchasing the grouped promotional commodities, determining strategies for recommending the grouped promotional commodities to the user groups according to the probability of the user groups purchasing the grouped promotional commodities, recommending the grouped promotional commodities to the user groups according to the strategies, recommending the grouped promotional commodities to the user groups, recommending suitable grouped promotional commodities to the user groups, achieving personalized promotion recommendation, and improving the grouping success rate of the grouped commodities.
Fig. 3 is a schematic flow chart of another commodity recommendation method provided in the embodiment of the present disclosure, and the embodiment is based on the foregoing embodiment and is optimized. As shown in fig. 3, the product recommendation method according to this embodiment includes:
in step S310, a first knowledge graph is generated according to the association relationship between users and the interaction records of the users and the commodities, wherein the first knowledge graph comprises a plurality of commodities, a plurality of users and the association relationship between the commodities and the users.
The first knowledge-graph may be represented by individual triplets (h, r, t), such as (Zhang three, age, 21), (Liqu, clicked, item 1). Some basic data are needed for constructing the first knowledge graph, basic characteristics of a user can be obtained from basic information of the user, interaction records of the user and the commodity can be obtained from a user log, and basic characteristics of the commodity can be obtained from basic information of the commodity.
A first knowledge-graph is constructed from the triplets, and a simplified partial knowledge-graph is shown in fig. 4. Fig. 4 may be represented by the following triplets: (user 1, native, guangdong), (user 1, clicked, item 1), (user 1, purchased, item 2), (item 2, clicked, user 4), (guangdong, user's regular residence, user 3), (user 3, invite to register, user 2).
In step S320, the knowledge-graph is generated according to relationships between users in the first knowledge-graph.
After the first knowledge graph is constructed, the knowledge graph spectrum can be reconstructed, namely, only the relation from the user to the user is reserved. Taking fig. 4 as an example, the knowledge-graph obtained after reconstruction from the first knowledge-graph shown in fig. 4 is shown in fig. 5. The reconstruction method takes the user 1 as an example, the user 1 can go to the user 3, the user 4 and the user 2 along an arrow, but no other user passes through the user 3 and the user 4 when the user 1 goes to the user 4, so that the user 1, the user 3 and the user 4 are in a one-layer relationship, the user 2 needs to pass through the user 3 when the user 2 goes to the user 1, the user 2 is in a two-layer relationship with the user 1, and so on, the users can be connected with each other only for the users with the one-layer relationship.
In step S330, the users are grouped based on the knowledge graph to obtain a plurality of user groups.
After the knowledge graph of the user dimension is constructed in step S320, each user may be numbered in sequence, for example, about 500 ten thousand users, and the number is 1 to 500 ten thousand.
After numbering the users, 1 ten thousand random numbers are selected from 1 to 500 thousand, that is, 1 ten thousand users are randomly selected. With the 1 ten thousand users as the central points, the users who are in a one-level relationship with the users are found, and a 1 ten thousand user group is formed. Taking fig. 3 as an example, if the user 1 is selected as the center point, the user 1, the user 3, and the user 4 form a user group.
Since the center point is randomly selected, it may cause some users to be not scheduled to a user group, or to be scheduled to multiple user groups. For the first case, the user who is selected can be used as the central point to find the group join which is closest to the user, and if a plurality of groups are closest, one group is randomly selected to join; for the second case, one group may be randomly assigned to retain the user and the remaining groups may be assigned to remove the user.
Through the operation, the grouping of the users is completed, and meanwhile, each user is ensured to have one and only one corresponding group. A vector is randomly initialized for each cluster, and the corresponding cluster is represented by the vector.
In step S340, any user group vector and any vector of the grouped promotional item are input into a pre-trained item purchase prediction model, and the probability that the user group purchases the grouped promotional item is obtained.
The steps involve the following:
(1) constructing a commodity purchase prediction model:
for example, a vector may be randomly initialized for each of the grouped promotional items.
(2) Constructing positive and negative samples of the commodity purchase prediction model training:
for example, a positive sample for each user may be defined as: defining the grouped sales promotion commodities clicked by all users in each user group as a positive sample of the user group, wherein the positive sample of the user group is the positive sample of each user in the group;
for the negative examples of each user, a part of the grouped sales promotion items that are not clicked by each user group may be randomly selected as the negative examples of the user group, and for example, the number of the negative examples may be controlled to be 3: 1, in a ratio of 1.
(3) Constructing a commodity purchase prediction model:
for example, the initialized goods purchase prediction model can be set as a neural network model with a structure having two hidden layer neural networks and one softmax layer. The activation function of each hidden layer neural network can be set as a ReLU function, the input of the model is a vector of a user (the vector of the user is a user group vector of the user) and a vector of a piecing together promotion commodity, the output of the model is a value between 0 and 1, the probability that the user clicks the commodity can be understood, the larger the value is, the more likely the user clicks the commodity is represented, and the threshold value is set to be 0.5, namely when the output value of the model is greater than or equal to 0.5, the user can be considered to click the commodity, and when the output value is less than 0.5, the user is considered not to click the commodity.
In step S350, for any user group, the grouped sales-promotion product with the highest probability is selected from the grouped sales-promotion products, and recommended to all users of the user group.
After the model is built and trained, when the user is predicted, only the vector of the user and the vector of the grouped sales promotion commodities are input into the model, and the commodity with the highest score is selected and recommended to the user. Because the vectors of users within each user group are the same, the recommended items seen by users within each user group are the same and the items are of most interest to them.
According to the embodiment, the knowledge graph is used for grouping the users, different grouping promotion commodities are recommended to each user group, personalized recommendation can be achieved, and grouping success rate of grouping promotion commodities can be improved.
As an implementation of the methods shown in the above figures, the present application provides an embodiment of a product recommendation device, and fig. 6 shows a schematic structural diagram of a product recommendation device provided in this embodiment, where the embodiment of the device corresponds to the method embodiments shown in fig. 1 to fig. 5, and the device may be specifically applied to various electronic devices. As shown in fig. 6, the product recommendation apparatus according to this embodiment includes a knowledge graph obtaining unit 610, a user clustering unit 620, a probability prediction unit 630, and a policy determination and product recommendation unit 640.
The knowledge graph acquiring unit 610 is configured to acquire a knowledge graph including a plurality of users and associations between the users according to the associations between the users and interaction records between the users and commodities.
The user clustering unit 620 is configured to cluster the plurality of users to obtain a plurality of user clusters based on the knowledge-graph.
The probability prediction unit 630 is configured to input any user group vector and any piece-together promoted product vector into a pre-trained product purchase prediction model, and obtain the probability that the user group purchases the piece-together promoted product.
The policy determining and goods recommending unit 640 is configured to determine a policy for recommending the plurality of grouped promoted goods to the plurality of user groups according to the probability of each group of users purchasing each grouped promoted goods, and recommend the plurality of grouped promoted goods to the plurality of user groups according to the policy.
In an embodiment, the knowledge-graph obtaining unit 610 is configured to further: generating a first knowledge graph according to the incidence relation among users and the interaction records of the users and the commodities, wherein the first knowledge graph comprises a plurality of commodities, a plurality of users and the incidence relation among the commodities and the users; and generating the knowledge graph according to the relation between users in the first knowledge graph.
In an embodiment, the user clustering unit 620 is configured to further:
randomly selecting a first number of users from the knowledge-graph as a central user group;
regarding any central user in the central user group, taking the central user and the user associated with the central user as a user group;
if any user does not belong to any user group, the user is added into the central user group, if any user belongs to a plurality of user groups, the user is randomly added into any user group, and the user is removed from other user groups.
In an embodiment, the policy determination and goods recommendation unit 640 is configured to further:
and for any user group, selecting the grouped sales promotion commodities with the highest probability from the grouped sales promotion commodities, and recommending the grouped sales promotion commodities to all users of the user group.
In one embodiment, the user group vector is an average vector of vectors of users included in the user group.
In one embodiment, the interaction records include one or more of a purchase record, a collection record, a click record, an evaluation record, and a return record.
In one embodiment, the association relationship between the users includes one or more of an invitation relationship and a geographical relationship, wherein the geographical relationship includes a place of origin and/or a place of residence.
Fig. 7 is a schematic structural diagram of a training module of the commodity purchase prediction model provided in this embodiment, and as shown in fig. 7, the commodity purchase prediction model is obtained by training through the sample obtaining module 710, the model determining module 720, and the model training module 730:
the sample acquiring module 710 is configured to acquire a training sample set, where a training sample includes a user group vector, a vector of a commodity, and a label representing a probability of the user group purchasing the commodity.
The model determination module 720 is configured for determining an initialized merchandise purchase prediction model, wherein the initialized merchandise purchase prediction model comprises a target layer for outputting a probability of a group of users purchasing merchandise.
The model training module 730 is configured to, by using a machine learning apparatus, train the user group vector and the commodity vector in the training samples in the training sample set as inputs of the initialized commodity purchase prediction model, and train the label corresponding to the input user group vector and the commodity vector as an output of the initialized commodity purchase prediction model to obtain the commodity purchase prediction model.
Further, the initialized commodity purchase prediction model comprises an input layer, a first hidden layer, a second hidden layer, a softmax layer and an output layer, wherein the first hidden layer and the second hidden layer adopt a ReLU function as an activation function.
The commodity recommendation device provided by the embodiment can execute the commodity recommendation method provided by the embodiment of the method disclosed by the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Referring now to FIG. 8, shown is a schematic diagram of an electronic device 800 suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 809, or installed from the storage means 808, or installed from the ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium described above in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the disclosed embodiments, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the disclosed embodiments, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
acquiring a knowledge graph according to the incidence relation between users and the interaction records of the users and the commodities, wherein the knowledge graph comprises a plurality of users and the incidence relation between the users;
grouping the plurality of users based on the knowledge graph to obtain a plurality of user groups;
inputting any user group vector and any vector of the grouped sales promotion commodities into a pre-trained commodity purchase prediction model to obtain the probability of the user group for purchasing the grouped sales promotion commodities;
and determining a strategy for recommending the grouped sales promotion commodities to the user groups according to the probability of purchasing the grouped sales promotion commodities by the user groups, and recommending the grouped sales promotion commodities to the user groups according to the strategy.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
The foregoing description is only a preferred embodiment of the disclosed embodiments and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure in the embodiments of the present disclosure is not limited to the particular combination of the above-described features, but also encompasses other embodiments in which any combination of the above-described features or their equivalents is possible without departing from the scope of the present disclosure. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims (11)

1. A method for recommending an article, comprising:
acquiring a knowledge graph according to the incidence relation between users and the interaction records of the users and the commodities, wherein the knowledge graph comprises a plurality of users and the incidence relation between the users;
grouping the plurality of users based on the knowledge graph to obtain a plurality of user groups;
inputting any user group vector and any vector of the grouped sales promotion commodities into a pre-trained commodity purchase prediction model to obtain the probability of the user group for purchasing the grouped sales promotion commodities;
and determining a strategy for recommending the grouped sales promotion commodities to the user groups according to the probability of purchasing the grouped sales promotion commodities by the user groups, and recommending the grouped sales promotion commodities to the user groups according to the strategy.
2. The method of claim 1, wherein obtaining the knowledge graph according to the association relationship between the users and the interaction records of the users and the commodities comprises:
generating a first knowledge graph according to the incidence relation among users and the interaction records of the users and the commodities, wherein the first knowledge graph comprises a plurality of commodities, a plurality of users and the incidence relation among the commodities and the users;
and generating the knowledge graph according to the relation between users in the first knowledge graph.
3. The method of claim 1, wherein clustering the plurality of users based on the knowledge-graph to obtain a plurality of user groups comprises:
randomly selecting a first number of users from the knowledge-graph as a central user group;
regarding any central user in the central user group, taking the central user and the user associated with the central user as a user group;
if any user does not belong to any user group, the user is added into the central user group, if any user belongs to a plurality of user groups, the user is randomly added into any user group, and the user is removed from other user groups.
4. The method of claim 1, wherein determining a policy for recommending the plurality of groups of users for the plurality of groups of users based on a probability that each group of users purchased each of the plurality of groups of users for a respective piece of the piece-together promotional merchandise comprises:
and for any user group, selecting the grouped sales promotion commodities with the highest probability from the grouped sales promotion commodities, and recommending the grouped sales promotion commodities to all users of the user group.
5. The method of claim 1 wherein the any one of the plurality of user group vectors is an average of vectors of users included in the user group.
6. The method of claim 1, wherein the interaction records include one or more of a purchase record, a collection record, a click record, an evaluation record, and a return record;
the association relationship between the users comprises one or more of an invitation relationship and a geographical relationship, wherein the geographical relationship comprises a native place and/or a residential place.
7. The method of any one of claims 1 to 6, wherein the commodity purchase prediction model is trained by:
acquiring a training sample set, wherein the training sample comprises a user group vector, a commodity vector and a label for representing the probability of purchasing the commodity by the user group;
determining an initialized commodity purchase prediction model, wherein the initialized commodity purchase prediction model comprises a target layer for outputting a probability of a user group purchasing a commodity;
and training to obtain the commodity purchase prediction model by using a machine learning method, wherein the user group vector and the commodity vector in the training samples in the training sample set are used as the input of the initialized commodity purchase prediction model, and the label corresponding to the input user group vector and the commodity vector is used as the output of the initialized commodity purchase prediction model.
8. The method of claim 7, wherein the initialized commodity purchase prediction model comprises an input layer, a first hidden layer, a second hidden layer, a softmax layer, and an output layer, the first hidden layer and the second hidden layer employing a ReLU function as an activation function.
9. An article recommendation device, comprising:
the system comprises a knowledge graph acquisition unit, a commodity interaction unit and a commodity interaction unit, wherein the knowledge graph acquisition unit is used for acquiring a knowledge graph according to the association relationship among users and the interaction records of the users and commodities, and the knowledge graph comprises a plurality of users and the association relationship among the users;
the user clustering unit is used for clustering the plurality of users based on the knowledge graph to obtain a plurality of user groups;
the probability prediction unit is used for inputting any user group vector and any vector of the grouped sales promotion commodities into a commodity purchasing prediction model trained in advance to obtain the probability of the user group for purchasing the grouped sales promotion commodities;
and the strategy determining and commodity recommending unit is used for determining a strategy for recommending the plurality of grouping promotion commodities to the plurality of user groups according to the probability of purchasing each grouping promotion commodity by each user group, and recommending the plurality of grouping promotion commodities to the plurality of user groups according to the strategy.
10. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
instructions which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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