A content recommendation method and device, computer device and medium

Abstract

The application provides a content recommendation method and device, computer equipment and medium, wherein the method comprises: obtaining data information and index proportion, and calculating the push proportion of each category based on the index proportion and the data information; and recommending corresponding content to the customer within the revision duration according to the push proportion of each category. The application has the beneficial effect that the user can comprehensively browse related content, reduces the phenomenon of overfitting push, enables the user to access diversified information, obtains information of different products, and improves the shopping experience and satisfaction of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of big data, and particularly relates to a content recommendation method and device, computer equipment and a medium. BACKGROUND

[0002] On an e-commerce platform, the existing content recommendation is generally based on the search information of a user to recommend related content, and after the user browses the content, the user is recommended related content based on the browsing information of the user, which may cause overfitting of the content recommended to the user, that is, the same category of content is recommended, so that the user ignores information of other categories, and the expansion of the content of the goods cannot be pushed, which limits the opportunity of the user to contact diversified information, thereby affecting the shopping experience and satisfaction of the user. SUMMARY

[0003] The main purpose of the present application is to provide a content recommendation method, device, computer equipment and medium, which aims to solve the problem that the expansion of the content of the goods cannot be pushed in the prior art.

[0004] The present application provides a content recommendation method, comprising:

[0005] detecting whether the time length of the user not placing an order after the content is pushed exceeds a first preset time length;

[0006] if the first preset time length is exceeded, obtaining an index proportion of each category of the user, and obtaining data information of the user browsing each category of content in a plurality of sub-time periods in a set time period;

[0007] corresponding each data information to each recurrent unit in a long short-term memory network to obtain a browsing state; wherein the long short-term memory network comprises a plurality of recurrent units, the number of the recurrent units is the same as the number of the data information, one recurrent unit comprises an output state, new information, an input gate, an output gate and a forget gate, and the output state is connected with the forget gate, the new information, the input gate, the output gate and the output state of the previous recurrent unit respectively;

[0008] inputting the browsing state into a fully connected layer of the long short-term memory network to convert to a specified dimension to obtain a browsing state vector;

[0009] nonlinearly mapping the browsing state vector to the interval (0, 1) and calculating a linear distance from 1;

[0010] obtaining a standard value based on the index proportion, comparing the linear distance with the standard value to obtain a comparison value;

[0011] When the comparison value exceeds the preset value, one or more recurrent units are added to the long short-term memory network, and simulation data information is input into the newly added recurrent units and the simulation data information is continuously adjusted until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulation data information;

[0012] Setting a revision duration according to the target simulation data information;

[0013] The push weight of each category is adjusted based on the target simulation data information, and corresponding content is recommended to the customer within the revised time period according to the push weight of each category.

[0014] Furthermore, the step of inputting each piece of data information into each recurrent unit in the long short-term memory network in a one-to-one correspondence to obtain a browsing state includes:

[0015] Input each of the data information into the forget gate, input gate, new information, and output gate of each recurrent unit in the long short-term memory network;

[0016] According to formula F t =sigmoid(W xf |X t |+W hf H t-1 +b f )

[0017] I t =sigmoid(W xi |X t |+W hi H t-1 +b i )

[0018] O t =sigmoid(W xo |X t |+W ho H t-1 +b o )

[0019] N t =tanh(W xn |X t |+W hn H t-1 +b n )

[0020] C t =F t ⊙C t-1 +I t ⊙N t

[0021] Calculate the output state of each cycle unit in turn to obtain the browsing state of the last cycle unit output; where F t represents the tth forget gate, I t represents the t-th input gate, N t represents the tth new information, O t represents the t-th output gate, C t represents the t-th output state, X t Indicates the tth data information, W xf 、W hf 、b f 、W xi 、W hi 、b i 、W xo 、W ho 、b o 、W xn 、W hn 、b h These are all preset parameters.

[0022] Furthermore, before the step of recommending corresponding content to the customer within the revised time period according to the push proportion of each category, the method further includes:

[0023] Obtaining preference information for each category of content based on the data information for each category of content;

[0024] According to the preference information of each category of content, content information related to the preference information is obtained to recommend to the user.

[0025] Furthermore, the step of obtaining the user's weight of indicators for each category includes:

[0026] Obtain user's personal information and search results;

[0027] The weight of the user's indicators for each category is set according to the search content and the personal information.

[0028] Furthermore, the indicator proportion is a data volume proportion or a browsing time proportion.

[0029] Furthermore, the indicator proportion is a data volume proportion, and after the step of adjusting the push proportion of each category based on the target simulation data information and recommending corresponding content to the customer within the revised time period according to the push proportion of each category, the method further includes:

[0030] Determining whether the period of time during which the user has not placed an order after the revised period exceeds a second preset period of time;

[0031] If the second preset time length is exceeded, the time length information of the user browsing each category within the set revision time length is counted;

[0032] The time length proportion of each category is set according to the time length information;

[0033] The new data amount proportion is set according to the time length proportion;

[0034] The new push proportion is obtained based on the new data amount proportion;

[0035] The corresponding content is recommended to the customer according to the new push proportion.

[0036] Further, before the step of detecting whether the time length that the user does not place an order after the content is pushed exceeds the first preset time length, the method further comprises:

[0037] The search information of the user is obtained;

[0038] The related content is pushed to the user based on the search information.

[0039] The application also provides a content recommendation device, comprising:

[0040] The detection module is used to detect whether the time length that the user does not place an order after the content is pushed exceeds the first preset time length;

[0041] The acquisition module is used to acquire the index proportion of the user for each category and the data information of the user browsing the content of each category within multiple sub-time periods in a set time period if the first preset time length is exceeded;

[0042] The first input module is used to input each data information into each cycle unit in a long short-term memory network one by one to obtain a browsing state; wherein the long short-term memory network comprises multiple cycle units, the number of cycle units is the same as the number of data information, one cycle unit comprises an output state, new information, an input gate, an output gate and a forgetting gate, and the output state is connected with the forgetting gate, the new information, the input gate, the output gate and the output state of the previous cycle unit respectively;

[0043] The second input module is used to input the browsing state into a fully connected layer of the long short-term memory network to convert to a specified dimension to obtain a browsing state vector;

[0044] The mapping module is used to non-linearly map the browsing state vector to the (0, 1) interval and calculate the linear distance from 1;

[0045] The comparison module is used to obtain a standard value based on the index proportion, compare the linear distance with the standard value to obtain a comparison value;

[0046] an adding module, configured to add one or more recurrent units to the long short-term memory network when the comparison value exceeds a preset value, input simulation data information into the newly added recurrent units, and continuously adjust the simulation data information until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulation data information;

[0047] A setting module, configured to set a revision duration according to the target simulation data information;

[0048] The adjustment module is used to adjust the push weight of each category based on the target simulation data information, and recommend corresponding content to the customer within the revised time period according to the push weight of each category.

[0049] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0050] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0051] The present invention has the following beneficial effects: by obtaining data information and indicator weights, and calculating the push weights for each category based on the indicator weights and data information, corresponding content is recommended to customers within the revised time period according to the push weights for each category. This allows users to browse relevant content comprehensively, reduces the phenomenon of overfitting push notifications, and enables users to access diverse information and obtain information on different products, thereby improving their shopping experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a content recommendation method according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic block diagram of the structure of a content recommendation device according to an embodiment of the present invention;

[0054] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0055] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0056] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of the present application.

[0057] It should be noted that all directional indications, such as upper, lower, left, right, front, back, etc., are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. The connection can be direct connection or indirect connection.

[0058] The term "and / or" in the present application is only used to describe the association relationship of the associated objects, and can represent three relationships, for example, A and B can represent three cases of existence of A alone, existence of A and B, and existence of B alone.

[0059] In addition, the description such as "first", "second" in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection claimed by the present application.

[0060] With reference to Figure 1 The present application provides a content recommendation method, comprising:

[0061] S1: detecting whether the time length of the user not placing an order after the content pushing exceeds a first preset time length;

[0062] S2: if the first preset time length is exceeded, obtaining the index proportion of the user for each category, and obtaining the data information of the user browsing the content of each category in a plurality of sub-time periods in a set time period;

[0063] S3: input each of the data information into each of the recurrent units in the long short-term memory network one by one to obtain a browsing state; wherein the long short-term memory network comprises a plurality of recurrent units, the number of the recurrent units is the same as the number of the data information, one recurrent unit comprises an output state, new information, an input gate, an output gate and a forget gate, the output state is connected with the forget gate, the new information, the input gate, the output gate and the output state of the previous recurrent unit respectively;

[0064] S4: input the browsing state into the fully connected layer of the long short-term memory network to convert to a specified dimension to obtain a browsing state vector;

[0065] S5: non-linearly map the browsing state vector to the interval (0, 1) and calculate the linear distance from 1;

[0066] S6: obtain a standard value based on the index proportion, compare the linear distance with the standard value to obtain a comparison value;

[0067] S7: when the comparison value exceeds a preset value, add one or more recurrent units to the long short-term memory network, input simulated data information to the added recurrent units and continuously adjust the simulated data information until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulated data information;

[0068] S8: set a revision duration according to the target simulated data information;

[0069] S9: adjust the push proportion of each category based on the target simulated data information, and recommend the corresponding content to the customer within the revision duration according to the push proportion of each category.

[0070] As described in step S1 above, it is detected whether the time length of the user not placing an order after the content push exceeds a first preset time length. The first preset time length is a pre-set time length, which is usually determined based on business needs and user behavior data. For example, it can be set that if the user does not place an order within X hours after viewing the recommended content, it is considered that the user's interest in this type of content may weaken or shift.

[0071] As described in step S2 above, if the first preset duration has been exceeded, the user's indicator weights for each category are obtained, as well as data information on the user's browsing of content in each category within multiple sub-periods of the set time period. This means that after the duration has been exceeded, it is clear that the pushed content is not satisfactory to the user. Therefore, the user's indicator weights for each category can be obtained. Specifically, the indicator weights can be data volume or browsing duration. This can be obtained by user input, or one or more indicator weights can be set for the user based on user information and allowed to be selected by the user. Data information on the user's browsing of content in each category within multiple sub-periods of the set time period can be obtained. Specifically, this data information can be obtained by obtaining information on the data the user browses and recording it in the background. For example, when a user browses content, a time information is generated. At this time, the category information and time information of the browsed content can be obtained. Statistics can then be generated based on the category, thereby obtaining data information on the user's browsing of content in each category within multiple sub-periods of the set time period. The set time is a pre-set time, such as the first pre-set duration after the user enters the search information, or it can be another custom duration. Furthermore, the category is a pre-defined category, such as product type, size, origin, price, etc.

[0072] As described in steps S3-S5 above, each piece of data information is input one-to-one into each recurrent unit in the long short-term memory network to obtain a browsing state; wherein, the long short-term memory network includes multiple recurrent units, the number of the recurrent units is the same as the number of the data information, and one recurrent unit includes an output state, new information, an input gate, an output gate, and a forget gate, and the output state is respectively connected to the forget gate, the new information, the input gate, the output gate, and the output state of the previous recurrent unit; the browsing state is input into the fully connected layer of the long short-term memory network to convert it into a specified dimension to obtain a browsing state vector; the browsing state vector is nonlinearly mapped to the (0,1) interval, and the linear distance from 1 is calculated.

[0073] Specifically, the long short-term memory network includes multiple recurrent units, a softmax layer and a fully connected layer. The multiple recurrent units are connected in sequence until the last recurrent unit. The last recurrent unit is connected to the fully connected layer, and then the softmax layer is connected to the fully connected layer. A recurrent unit includes an output state, new information, an input gate, an output gate and a forget gate. The output state is respectively connected to the forget gate, the new information, the input gate, the output gate and the output state of the previous recurrent unit. The activation function of the new information layer is the tanh function, and the activation functions of the forget gate, input gate and output gate are the sigmoid function. After inputting the data information one by one, the corresponding browsing status can be obtained using the long short-term memory network. Due to the characteristics of the long short-term memory network, it can handle long-term dependencies and solve the gradient vanishing problem in the simple neural network model. By controlling the information flow through the gating mechanism, more complex dependencies can be captured. Therefore, the browsing status obtained can accurately summarize the user's browsing situation, and then input it into the fully connected layer of the long short-term memory network to convert it into a specified dimension to obtain a browsing state vector. The fully connected layer can convert the input browsing state into a browsing state vector of the specified dimension, and then perform sigmoid mapping on it to the (0,1) interval, perform normalization processing, and calculate the linear distance between it and 1, specifically subtracting the mapping value from 1.

[0074] As described in the above steps S6-S7, a standard value is obtained based on the indicator proportion, and the linear distance is compared with the standard value to obtain a comparison value. Among them, the standard value is obtained according to the indicator proportion, that is, a correspondence table between the indicator proportion and the standard value can be pre-set, and then after obtaining the indicator proportion, the corresponding standard value can be obtained. The specific correspondence is not limited and can be determined manually in advance. It should be noted that the standard value needs to be determined in combination with the linear distance, that is, a determined indicator proportion needs to correspond to a standard linear distance, and the standard value is the linear distance of the standard. Then, the linear distance is compared with the standard value to obtain a comparison value. When the comparison value exceeds the preset value, one or more recurrent units are added to the long short-term memory network, and simulation data information is input into the newly added recurrent unit and the simulation data information is continuously adjusted until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulation data information. Among them, the preset value is a pre-set value. When it exceeds the preset value, it is considered that the weights of the various categories of content recommended to the user are unreasonable. When it is less than or equal to the preset value, it is considered that the weights of the various categories of content recommended to the user are reasonable. At this time, one or more loop units are added thereto, and the newly added loop units input simulation data information and continuously adjust the simulation data information until the comparison value is less than or equal to the preset value. In fact, this is to simulate the data of the content categories recommended to the user subsequently. When the comparison value is less than or equal to the preset value, the target simulation data information can be obtained.

[0075] As described in steps S8-S9 above, since the target simulation data may vary in quantity, it is necessary to determine a revised duration (i.e., the length of time allowed for user browsing) based on the specific amount of data. Then, based on the target simulation data, the push weights for each category are adjusted, and corresponding content is recommended to the customer within the revised duration according to the push weights for each category. This allows users to browse relevant content comprehensively, reduces overfitting push notifications, and allows users to access diverse information and obtain information on different products, thereby improving their shopping experience and satisfaction.

[0076] In one embodiment, the step S3 of inputting each piece of data information into each recurrent unit in the long short-term memory network in a one-to-one correspondence to obtain a browsing state includes:

[0077] S301: Inputting each of the data information into the forget gate, input gate, new information, and output gate of each recurrent unit in the long short-term memory network;

[0078] S302: According to formula F t =sigmoid(W xf |X t |+W hf H t-1 +bf )

[0079] I t =sigmoid(W xi |X t |+W hi H t-1 +b i )

[0080] O t =sigmoid(W xo |X t |+W ho H t-1 +b o )

[0081] N t =tanh(W xn |X t |+W hn H t-1 +b n )

[0082] C t =F t ⊙C t-1 +I t ⊙N t

[0083] Calculate the output state of each cycle unit in turn to obtain the browsing state output by the last cycle unit; where F t represents the tth forget gate, I t represents the t-th input gate, N t represents the tth new information, O t represents the t-th output gate, C t represents the t-th output state, X t Indicates the tth data information, W xf 、W hf 、b f 、W xi 、W hi 、b i 、W xo 、W ho 、b o 、W xn 、W hn 、b n These are all preset parameters.

[0084] As described in steps S301-S302 above, the browsing status is obtained. Since each output state is a combination of the output state of the previous cycle unit and the newly added information, and of course its weight is determined by the input gate and the forget gate, the current cycle unit can obtain information about the data of the previous cycle unit, so that the browsing status can accurately summarize the user's browsing situation.

[0085] In one embodiment, before step S9 of recommending corresponding content to the customer within the revised time period according to the push proportion of each category, the method further includes:

[0086] S801: Obtaining preference information of each category of content based on the data information of each category of content;

[0087] S802: According to the preference information of each category of content, obtain content information related to the preference information to recommend to the user.

[0088] As described in steps S801-S802 above, even within the same category, users may have different preferences for recommended content. For example, a user may prefer a certain host or a certain brand. Therefore, preference information for content in each category can be obtained from previous data information, and then content information related to the preference information can be obtained to recommend to users.

[0089] In one embodiment, the step S2 of obtaining the weight of the user's indicators for each category includes:

[0090] S201: Obtaining the user's personal information and the user's search content;

[0091] S202: Setting the weight of the user's indicators for each category according to the search content and the personal information.

[0092] As described in the above steps S201-S202, the indicator weights are determined, and the user's personal information and the user's search content are obtained. The user's personal information may contain the user's preferences. For example, when buying snack products, the user may prefer spicy strips produced in Guizhou, but the user searches for spicy strips. Therefore, different spicy strips can have different indicator weights, and more weights can be set for spicy strips that users often buy.

[0093] In one embodiment, the indicator weight is a data volume weight or a browsing time weight.

[0094] In one embodiment, the indicator weight is a data volume weight. After the step S9 of adjusting the push weight of each category based on the target simulation data information and recommending corresponding content to the customer within the revised time period according to the push weight of each category, the method further includes:

[0095] S1001: Determine whether the time period during which the user has not placed an order after the revised time period exceeds a second preset time period;

[0096] S1002: If the second preset time period is exceeded, then statistics are collected on the time period that the user browses each category within the set revised time period;

[0097] S1003: Setting the duration ratio of each category according to the duration information;

[0098] S1004: Setting a new data volume ratio according to the duration ratio;

[0099] S1005: Obtain a new push proportion based on the new data volume proportion;

[0100] S1006: Recommending corresponding content to the customer based on the new push ratio.

[0101] As described in the above steps S1001-S1006, a secondary push of content is achieved. If the time period during which the user has not placed an order after the revised time period exceeds the second preset time period, it means that the user is still dissatisfied with the recommendation. Therefore, the time information of the user browsing each category can be obtained, and the time proportion of each category can be set according to the time information, and the time proportion is used as the new data volume proportion. Then, a new push proportion is obtained based on the new data volume proportion. The method of obtaining the new push proportion based on the data volume proportion is the same as that of the above steps S3-S9, and will not be repeated here. The corresponding content is recommended to the customer based on the new push proportion, so that the expanded information is further fitted on the premise of previously expanding the information for the user, so that the secondary recommended content can be closer to the user, thereby improving the user experience.

[0102] In one embodiment, before step S1 of detecting whether the time period during which the user has not placed an order after content push exceeds a first preset time period, the method further includes:

[0103] S001: Obtain user's search information;

[0104] S002: Pushing relevant content to the user based on the search information.

[0105] As described in the above steps S001-S002, that is, after obtaining the user's search information, relevant content can be pushed to the user first. The information of the pushed content can be a push method pre-set in the e-commerce platform.

[0106] Reference Figure 2 The present invention also provides a content recommendation device, comprising:

[0107] Detection module 10, used to detect whether the time period during which the user has not placed an order after the content is pushed exceeds a first preset time period;

[0108] An acquisition module 20 is configured to acquire the weight of the user's indicators for each category and acquire data information on the user browsing content of each category within multiple sub-time periods of the set time period if the first preset time period is exceeded;

[0109] A first input module 30 is configured to input each piece of data information into each recurrent unit in the long short-term memory network in a one-to-one correspondence to obtain a browsing state; wherein the long short-term memory network includes a plurality of recurrent units, the number of which is the same as the number of pieces of data information, and a recurrent unit includes an output state, new information, an input gate, an output gate, and a forget gate, wherein the output state is respectively connected to the forget gate, the new information, the input gate, the output gate, and the output state of the previous recurrent unit;

[0110] A second input module 40 is configured to input the browsing state into a fully connected layer of the long short-term memory network to convert the browsing state into a specified dimension to obtain a browsing state vector;

[0111] A mapping module 50 is used to nonlinearly map the browsing state vector to the interval (0, 1) and calculate the linear distance from 1;

[0112] A comparison module 60 is configured to obtain a standard value based on the indicator proportion, compare the linear distance with the standard value, and obtain a comparison value;

[0113] an adding module 70 for adding one or more recurrent units to the long short-term memory network when the comparison value exceeds a preset value, inputting simulation data information into the newly added recurrent units, and continuously adjusting the simulation data information until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulation data information;

[0114] A setting module 80 is configured to set a revision duration according to the target simulation data information;

[0115] The adjustment module 90 is configured to adjust the push weight of each category based on the target simulation data information, and recommend corresponding content to the customer within the revised time period according to the push weight of each category.

[0116] In one embodiment, the first input module 30 includes:

[0117] A data information input submodule, used to input each of the data information into the forget gate, input gate, new information, and output gate of each recurrent unit in the long short-term memory network;

[0118] Output state calculation submodule is used to calculate the output state according to the formula F t =sigmoid(W xf |X t |+W hf H t-1 +b f )

[0119] I t =sigmoid(W xi |X t |+W hi H t-1 +b i )

[0120] O t =sigmoid(W xo |X t |+W ho H t-1 +b o )

[0121] N t =tanh(W xn |X t |+W hn H t-1 +b n )

[0122] C t =F t ⊙C t-1 +I t ⊙N t

[0123] Calculate the output state of each cycle unit in turn to obtain the browsing state output by the last cycle unit; where F t represents the tth forget gate, I t represents the t-th input gate, N t represents the tth new information, O t represents the t-th output gate, C t represents the t-th output state, X t Indicates the tth data information, W xf 、W hf 、b f 、W xi 、W hi 、b i 、W xo 、W ho 、bo 、W xn 、W hn 、b h These are all preset parameters.

[0124] In one embodiment, the content recommendation device further includes:

[0125] A preference information acquisition module, configured to acquire preference information of each category of content based on the data information of each category of content;

[0126] The content information acquisition module is used to acquire content information related to the preference information based on the preference information of each category of content, so as to recommend it to the user.

[0127] In one embodiment, the acquisition module 20 includes:

[0128] The search content acquisition submodule is used to obtain the user's personal information and the user's search content;

[0129] The indicator weight setting submodule is used to set the indicator weight of the user for each category according to the search content and the personal information.

[0130] In one embodiment, the indicator weight is a data volume weight or a browsing time weight.

[0131] In one embodiment, the indicator proportion is a data volume proportion, and the content recommendation device further includes:

[0132] A duration determination module, configured to determine whether the time during which the user has not placed an order after the revised time exceeds a second preset time;

[0133] A duration information statistics module is used to count the duration information of the user browsing each category within the set revised duration if the second preset duration is exceeded;

[0134] A duration weight setting module, configured to set the duration weight of each category according to the duration information;

[0135] A data volume weight setting module, configured to set a new data volume weight according to the duration weight;

[0136] A new push weight acquisition module, configured to acquire a new push weight based on the new data volume weight;

[0137] The content recommendation module is used to recommend corresponding content to customers according to the new push proportion.

[0138] In one embodiment, the content recommendation device further includes:

[0139] A search information acquisition module is used to obtain the user's search information;

[0140] The related content pushing module is configured to push the related content to the user based on the search information.

[0141] The present application has the following advantages: by obtaining data information and an index proportion, and calculating a push proportion of each category based on the index proportion and the data information, the corresponding content is recommended to the customer within the revision duration according to the push proportion of each category. Thus, the user can comprehensively browse the related content, the phenomenon of overfitting push is reduced, the user can access diversified information, obtain information of different products, and the shopping experience and satisfaction of the user are improved.

[0142] Reference Figure 3 In the embodiments of the present application, a computer device is also provided, which can be a server, and the internal structure thereof can be as shown in Figure 3 The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store various data information. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program, when executed by the processor, can implement the content recommendation method described in any of the above embodiments.

[0143] Those skilled in the art can understand that Figure 3 The structure shown in the above embodiments is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied.

[0144] The embodiments of the present application also provide a computer readable storage medium having a computer program stored thereon, and the computer program, when executed by the processor, can implement the content recommendation method described in any of the above embodiments.

[0145] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments can be included. Any reference to memory, storage, databases, or other media in this application and in examples provided herein can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), or external cache memory. As an illustration but not limitation, RAM can be available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0146] It should be noted that in this paper, the term "including", "containing" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, device, article or method. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0147] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence (AI) is the use of digital computers or digital computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0148] The basic technology of artificial intelligence generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0149] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of the claims.

Claims

1. A content recommendation method, characterized in that: include: Detect whether the time period during which the user has not placed an order after the content is pushed exceeds a first preset time period; If the first preset time period is exceeded, obtaining the user's indicator proportion for each category, and obtaining data information on the user browsing content of each category within multiple sub-time periods of the set time period; Inputting each piece of data information into each recurrent unit in a long short-term memory network in a one-to-one correspondence to obtain a browsing state; wherein the long short-term memory network includes a plurality of recurrent units, the number of the recurrent units is the same as the number of the data information, and a recurrent unit includes an output state, new information, an input gate, an output gate, and a forget gate, and the output state is respectively connected to the forget gate, the new information, the input gate, the output gate, and the output state of the previous recurrent unit; Inputting the browsing state into the fully connected layer of the long short-term memory network to convert it into a specified dimension to obtain a browsing state vector; Nonlinearly mapping the browsing state vector to the interval (0, 1) and calculating the linear distance from 1; A standard value is obtained based on the index proportion, and the linear distance is compared with the standard value to obtain a comparison value; When the comparison value exceeds the preset value, one or more recurrent units are added to the long short-term memory network, and simulation data information is input into the newly added recurrent units and the simulation data information is continuously adjusted until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulation data information; Setting a revision duration according to the target simulation data information; The push weight of each category is adjusted based on the target simulation data information, and corresponding content is recommended to the customer within the revised time period according to the push weight of each category.

2. The content recommendation method according to claim 1, wherein: The step of inputting each piece of data information into each recurrent unit in the long short-term memory network one by one to obtain a browsing state includes: Input each of the data information into the forget gate, input gate, new information, and output gate of each recurrent unit in the long short-term memory network; According to formula F t =sigmoid(W xf |X t |+W hf H t-1 +b f ) I t =sigmoid(W xi |X t |+W hi H t-1 +b i ) O t =sigmoid(W xo |X t |+W ho H t-1 +b o ) N t =tanh(W xn |X t |+W hn H t-1 +b n ) C t =F t ⊙C t-1 +I t ⊙N t Calculate the output state of each cycle unit in turn to obtain the browsing state output by the last cycle unit; where F t represents the tth forget gate, I t represents the t-th input gate, N t Indicates the tth new information, O t represents the t-th output gate, C t represents the t-th output state, X t Indicates the tth data information, W xf 、W hf 、b f 、W xi 、W hi 、b i 、W xo 、W ho 、b o 、W xn 、W hn 、b n These are all preset parameters.

3. The content recommendation method according to claim 1, wherein: Before the step of recommending corresponding content to the customer within the revised time period according to the push proportion of each category, the method further includes: Obtaining preference information for each category of content based on the data information for each category of content; According to the preference information of each category of content, content information related to the preference information is obtained to recommend to the user.

4. The content recommendation method according to claim 1, wherein: The step of obtaining the user's indicator weight for each category includes: Obtain user's personal information and search results; The weight of the user's indicators for each category is set according to the search content and the personal information.

5. The content recommendation method according to claim 1, wherein: The indicator proportion is the data volume proportion or the browsing time proportion.

6. The content recommendation method according to claim 1, wherein: The indicator proportion is the data volume proportion. After the step of adjusting the push proportion of each category based on the target simulation data information and recommending corresponding content to the customer within the revised time period according to the push proportion of each category, the method further includes: Determining whether the period of time during which the user has not placed an order after the revised period exceeds a second preset period of time; If the second preset time period is exceeded, the time information of the user browsing each category within the set revised time period is counted; Setting a duration ratio for each category according to the duration information; Setting a new data volume ratio according to the duration ratio; Obtaining a new push weight based on the new data volume weight; Recommend corresponding content to the customer based on the new push ratio.

7. The content recommendation method according to claim 1, wherein: Before the step of detecting whether the time period during which the user has not placed an order after the content is pushed exceeds a first preset time period, the method further includes: Get the user's search information; Push relevant content to the user based on the search information.

8. A content recommendation device, characterized in that: include: A detection module, configured to detect whether the time period during which the user has not placed an order after the content has been pushed exceeds a first preset time period; an acquisition module, configured to acquire the weight of the user's indicators for each category if the first preset time period is exceeded, and acquire data information on the user browsing content of each category within multiple sub-time periods of the set time period; A first input module is configured to input each piece of data information into each recurrent unit in the long short-term memory network in a one-to-one correspondence to obtain a browsing state; wherein the long short-term memory network includes a plurality of recurrent units, the number of which is the same as the number of the data information, and a recurrent unit includes an output state, new information, an input gate, an output gate, and a forget gate, wherein the output state is respectively connected to the forget gate, the new information, the input gate, the output gate, and the output state of the previous recurrent unit; A second input module is used to input the browsing state into the fully connected layer of the long short-term memory network to convert it into a specified dimension to obtain a browsing state vector; A mapping module, configured to nonlinearly map the browsing state vector to the interval (0, 1) and calculate the linear distance from 1; A comparison module, configured to obtain a standard value based on the index proportion, and compare the linear distance with the standard value to obtain a comparison value; an adding module, configured to add one or more recurrent units to the long short-term memory network when the comparison value exceeds a preset value, input simulation data information into the newly added recurrent units, and continuously adjust the simulation data information until the comparison value is less than or equal to the preset value, thereby obtaining the final adjusted target simulation data information; A setting module, configured to set a revision duration according to the target simulation data information; The adjustment module is used to adjust the push weight of each category based on the target simulation data information, and recommend corresponding content to the customer within the revised time period according to the push weight of each category.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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