Transaction combination recommendation method and device, equipment, storage medium and program product

Abstract

The invention provides a transaction combination recommendation method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology or other related fields. The method comprises the steps that user feature information of a target user is acquired, a current session sequence is obtained through conversion based on current transaction data of the target user, the current session sequence is input into a first prediction model, the first prediction model abstracts the current session sequence into a sequence feature vector based on an attention mechanism, and the sequence feature vector is input into a second prediction model; and generating a first recommendation result based on the sequence feature vector, further obtaining a second recommendation result according to the first recommendation result and the user feature information, and then determining a target transaction combination recommended to the target user according to the second recommendation result. According to the method provided by the invention, accurate and personalized transaction recommendation service is provided for the user.

Description

Technical Field

[0001] The present application relates to the field of financial technology or other related fields, and in particular to a transaction combination recommendation method, device, equipment, storage medium and program product. Background Art

[0002] With the rapid development of science and technology and the digital transformation of the financial industry, bank self-service terminal transaction systems have become an important part of modern financial services. In the context of branch operation reform, traditional counter transactions have gradually shifted to self-service terminals. At present, branch users tend to be aging and face an increasing number of products and services. With limited self-service terminal equipment, how to help users quickly find the transaction combination that best suits their needs has become an important direction for improving the efficiency of self-service terminals.

[0003] In the related art, when a user conducts a business transaction based on a self-service terminal, the SR-GNN algorithm is used to represent the user's current transaction data in the form of a graph structure, and then the recommended transaction is obtained by capturing the spatial proximity and correlation between different transactions of the user in the graph structure, and when the user completes the business transaction based on the self-service terminal, the recommended transaction is automatically displayed on the self-service terminal interface. However, this method cannot provide accurate and personalized transaction recommendation services. Summary of the invention

[0004] The present application provides a transaction combination recommendation method, device, equipment, storage medium and program product to solve the technical problem that the related technology cannot provide accurate and personalized transaction recommendation services.

[0005] In a first aspect, the present application provides a transaction combination recommendation method, comprising: obtaining a current session sequence and user feature information of a target user, wherein the current session sequence is obtained by converting current transaction data of the target user; inputting the current session sequence into a first prediction model, wherein the first prediction model abstracts the current session sequence into a sequence feature vector based on an attention mechanism, and generates a first recommendation result based on the sequence feature vector; obtaining a second recommendation result based on the first recommendation result and the user feature information; and determining a target transaction combination recommended to the target user based on the second recommendation result.

[0006] In a second aspect, the present application provides a transaction combination recommendation device, comprising:

[0007] An acquisition module is used to acquire the current session sequence and user feature information of the target user, where the current session sequence is obtained by converting the current transaction data of the target user;

[0008] A generation module, configured to input the current conversation sequence into a first prediction model, wherein the first prediction model abstracts the current conversation sequence into a sequence feature vector based on an attention mechanism, and generates a first recommendation result based on the sequence feature vector;

[0009] A prediction module, used to obtain a second recommendation result based on the first recommendation result and user characteristic information;

[0010] The determination module is used to determine the target transaction combination recommended to the target user according to the second recommendation result.

[0011] In a third aspect, the present application provides an electronic device comprising a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the transaction combination recommendation method provided in the first aspect above.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer execution instructions. When the computer execution instructions are executed by a processor, they are used to implement the transaction combination recommendation method provided in the first aspect above.

[0013] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the transaction combination recommendation method provided in the first aspect above.

[0014] The transaction combination recommendation method, device, equipment, storage medium and program product provided by the present application obtain the user feature information of the target user and the current session sequence converted based on the current transaction data of the target user, input the current session sequence into the first prediction model, so that the first prediction model abstracts the current session sequence into a sequence feature vector based on the attention mechanism, and generates a first recommendation result based on the sequence feature vector, and further obtains the second recommendation result based on the first recommendation result and the user feature information, and then determines the target transaction combination recommended to the target user based on the second recommendation result. The present application predicts the current session sequence of the target user based on the transaction sequence features through the first prediction model, and integrates the user feature information on the first recommendation result predicted based on the transaction sequence features to achieve prediction based on user features, so as to provide accurate and personalized transaction recommendation services to the target user. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0016] Figure 1 A schematic diagram of transaction functions of a self-service terminal provided in an embodiment of the present application;

[0017] Figure 2 Schematic diagram of the process of the transaction combination recommendation method provided in the embodiment of the present application Figure 1 ;

[0018] Figure 3 A schematic diagram of the structure of the first prediction model provided in the embodiment of the present application;

[0019] Figure 4 A schematic diagram of the adjacency relationship of the session graph provided in an embodiment of the present application;

[0020] Figure 5 Schematic diagram of the process of the transaction combination recommendation method provided in the embodiment of the present application Figure 2 ;

[0021] Figure 6 A schematic diagram of the process of training the first prediction model and the second preset model provided in the embodiment of the present application;

[0022] Figure 7 A schematic diagram of the structure of the joint training of the first prediction model and the second prediction model provided in an embodiment of the present application;

[0023] Figure 8 A schematic diagram of the structure of a transaction combination recommendation device provided in an embodiment of the present application;

[0024] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0025] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0027] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0028] In addition, this application involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.), and the use of artificial intelligence technology for automated decision-making, and a technical solution for making decisions that have a significant impact on personal rights and interests based on the results of automated decision-making. The application provides users with corresponding operation entrances for them to choose to agree or reject the results of automated decision-making; if the user chooses to reject, the expert decision-making process will be entered.

[0029] It should be noted that the transaction combination recommendation method, device, equipment, storage medium and product provided in this application can be used in the field of financial technology, and can also be used in any field other than the field of financial technology. The application does not limit the application field of the transaction combination recommendation method, device, equipment, storage medium and product.

[0030] In the related technology, when a user completes a business transaction, the self-service terminal usually provides related recommended transactions or cross-selling suggestions. This practice helps banks or other institutions to increase the cross-selling rate and provide users with more choices and services. Currently, the common recommendation algorithms for user prediction and recommended transactions include traditional recurrent neural networks (RNN), Markov algorithms, and SR-GNN algorithms. However, these recommendation algorithms have the following problems:

[0031] 1) Traditional RNNs are prone to gradient vanishing or gradient exploding problems when processing long sequences, and are difficult to capture long-term temporal dependencies;

[0032] 2) Markov algorithms usually only consider limited historical information and cannot capture long-term dependencies. For continuous state spaces or high-dimensional data, Markov models have limitations;

[0033] 3) The SR-GNN algorithm represents the user's current transaction data in the form of a graph structure. Compared with traditional RNN and Markov algorithms, the SR-GNN algorithm can fully consider the spatial proximity and correlation between transactions. However, the SR-GNN model only has sequence features and is not combined with user feature data, so the personalized recommendation capability is insufficient.

[0034] Based on the technical problems existing in the related technology, the embodiment of the present application predicts the current session sequence of the target user based on the transaction sequence characteristics through a first prediction model, and integrates the user feature information on the first recommendation result based on the transaction sequence feature prediction to achieve prediction based on user features, so as to provide accurate and personalized transaction recommendation services to the target user.

[0035] The following first describes in detail the application scenarios of the embodiments of the present application.

[0036] An applicable scenario of the transaction combination recommendation method, device, equipment, storage medium and program product provided in the embodiments of the present application is to provide personalized transaction recommendation services to users when business transactions are conducted based on self-service terminals at the end of the business transactions.

[0037] Figure 1 This is a schematic diagram of the functional structure of the self-service terminal provided in the embodiment of the present application. Figure 1 As shown, when business transactions are conducted based on the self-service terminal, there are transactions of different levels in the self-service terminal, such as primary transactions, secondary transactions, and tertiary transactions.

[0038] like Figure 1 As shown in , for example, the first-level transaction can be financial management, account management, etc.; the second-level transaction can be password management and personal financial management, etc.; the third-level transaction can be my financial products and personal financial management details, etc.

[0039] It is understandable that there is a certain correlation between primary transactions, secondary transactions and tertiary transactions. For example, a primary transaction can include multiple secondary transactions, and each secondary transaction can include multiple tertiary transactions.

[0040] It can be understood that the above is only an application scenario applicable to the embodiment of the present application. The embodiment of the present application does not limit the specific application scenario, which can be determined according to actual application requirements.

[0041] The transaction combination recommendation method provided in this application is intended to solve the above technical problems of the prior art.

[0042] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0043] Figure 2 Schematic diagram of the process of the transaction combination recommendation method provided in the embodiment of the present application Figure 1 .like Figure 2 As shown, the specific implementation of the transaction combination recommendation method may include the following steps:

[0044] S201, obtaining a current session sequence and user feature information of a target user, wherein the current session sequence is obtained by converting current transaction data of the target user.

[0045] Exemplarily, the current transaction data of the target user may be business transaction data corresponding to a business transaction completed by the target user based on a self-service terminal.

[0046] For example, a business transaction may be as described above. Figure 1 The three-level transaction shown in .

[0047] It is understandable that when completing a business transaction, the target user needs to click on multiple transactions of different functional levels on the self-service terminal in sequence, that is, the current transaction data of the target user includes multiple business transactions clicked by the target user in sequence. The functional levels of the business transactions may be the same or different.

[0048] In a possible implementation, multiple business transactions included in the current transaction data of the target user are arranged in sequence according to the order in which they are clicked by the target user, so as to obtain a current session sequence corresponding to the current transaction data.

[0049] Exemplarily, when converting the current transaction data, the tertiary transactions contained therein are converted into corresponding secondary transactions based on the mapping relationship between the tertiary transaction identification (Identity, ID for short) and the secondary transaction identification ID.

[0050] For example, the user characteristics may include personal financial assets, age, region, and gender of the target user.

[0051] Exemplarily, the user characteristic information may be a user characteristic including a weight coefficient, such as a weight coefficient of personal financial assets being 0.3.

[0052] Exemplarily, the weight coefficients of different user characteristics are determined based on empirical data and the age of the target user, and this application does not limit this.

[0053] S202, inputting the current conversation sequence into a first prediction model, the first prediction model abstracting the current conversation sequence into a sequence feature vector based on an attention mechanism, and generating a first recommendation result based on the sequence feature vector.

[0054] Exemplarily, the first prediction model may be a SR-GNN model.

[0055] Let's first combine Figure 3 The first prediction model is described in detail.

[0056] Figure 3 This is a schematic diagram of the structure of the first prediction model provided in the embodiment of the present application. Figure 3 As shown, the first prediction model includes four parts: constructing a conversation graph 31, abstracting the self-service terminal transaction into a vector representation 32, abstracting the self-service terminal transaction conversation into a vector representation based on a self-attention network 33, and generating a recommendation result 34.

[0057] Among them, 1) Constructing a session graph: Given a session sequence S, construct a session graph:

[0058] G s =(V s ,ε s )

[0059] Among them, V s represents the number of nodes in the session graph, ε s Represents the number of directed edges.

[0060] For example, in the constructed session graph, each node can be represented by v i Indicates that each node represents a transaction in the conversation sequence S, and the connection between two adjacent nodes is a directed edge. That is, in the conversation sequence S, there is a directed edge between two transactions clicked by the user in sequence. The directed edge can be expressed as (v i-1 ,v i ).

[0061] For example, Figure 3 The conversation graph 310 shown in FIG. 3 includes (v1, v2, v4, v3), (v2, v5, v6, v7), and (v5, v4, v3, v6), etc., and the arrows represent directed edges consisting of two transactions clicked sequentially.

[0062] 2) Abstract the self-service terminal transaction into a vector representation:

[0063] Graph Neural Networks (GNN) is used to learn the unified representation of all nodes in the above session graph. Its main calculation propagation formula is:

[0064]

[0065] In formula (1), the connection matrix A is used s Integrate information from adjacent nodes and connect the matrix A s Determines how nodes in the session graph are connected. s From two adjacency matrices and Spliced ​​together, A s,i It represents the node v s,i Respectively and The two corresponding columns.

[0066] Formula (2) to Formula (5) are similar to the calculation process of the gated recurrent unit (GRU), where Formula (2) Indicates the control of forgetting information, formula (3) represents the control of the newly generated information, in formula (4) Indicates the decision of which historical information to generate new information from. represents the newly generated information, in formula (5) Indicates which historical information to choose to forget. Indicates which new information is selected to be remembered. Indicates the final updated node status.

[0067] Figure 4 A schematic diagram of the adjacency relationship of the session graph provided in the embodiment of the present application. Figure 4 As shown, the corresponding session sequence in the session graph is (v1, v2, v3, v2, v4) where the connection matrix A s From two adjacency matrices and Stitched together.

[0068] For example, the connection matrix A s ∈R n×2n .

[0069] 3) Based on the self-attention network, the self-service terminal transaction session is abstracted into a vector representation:

[0070] For each session in the self-service terminal transaction session, the attention mechanism is used to combine the global preference and the current preference for representation. The current preference can be represented by a local embedding to represent the last node in the session sequence. For example, the current preference is used to emphasize the influence of the last clicked transaction.

[0071] Exemplarily, the current preference can be expressed by the following formula:

[0072] S1=v n

[0073] Among them, S1 represents the current preference.

[0074] The global preference is obtained through the self-attention network, which can be represented by global embedding. The attention network assigns a weight coefficient to each transaction in the session, which is used to indicate the importance of each transaction to the entire session.

[0075] For example, the weight coefficient can be expressed by the following formula:

[0076] a i =q T σ(W1v n +W2v i +c)

[0077] Among them, a i Represents the weight coefficient, q, W1 and W2 control the weight.

[0078] Exemplarily, the global preference can be expressed by the following formula:

[0079]

[0080] Combine the current preference S1 with the global preference S g Splice together to get the final session.

[0081] Exemplarily, the final session can be expressed by the following formula:

[0082] S h =W3[S1;S g ]

[0083] Among them, S h Indicates the final session.

[0084] 4) Generate recommendation results:

[0085] The final session S h The dot product is performed with each transaction to calculate the recommendation score of each transaction, and the probability of each transaction is obtained through the softmax function.

[0086] For example, the probability of each transaction can be expressed by the following formula:

[0087]

[0088] Exemplarily, the first recommendation result may include multiple recommended transactions, wherein each recommended transaction has a different recommendation score.

[0089] S203: Obtain a second recommendation result based on the first recommendation result and the user characteristic information.

[0090] In a possible implementation, the first recommendation result is integrated with the user feature information to obtain a second recommendation result.

[0091] Exemplarily, the second recommendation result may include a plurality of different recommended transactions. Some of the recommended transactions in the second recommendation result may be the same as the recommended transactions in the first recommendation result, or all of them may be different.

[0092] S204: Determine a target transaction combination recommended to the target user based on the second recommendation result.

[0093] Illustratively, the target transaction combination may include a plurality of different recommended transactions belonging to secondary functional transactions.

[0094] For example, the number of recommended transactions in the target transaction combination may be 3 to 4. The transaction combination recommendation method provided in the embodiment of the present application does not limit the number of recommended transactions in the target transaction combination, and can be determined according to actual application requirements.

[0095] Exemplarily, when a target user completes a business transaction based on a self-service terminal, the target transaction combination is automatically displayed on the user interface of the self-service terminal to automatically recommend personalized transactions to the target user.

[0096] In the embodiment of the present application, the user feature information of the target user is obtained, and the current session sequence is obtained based on the conversion of the current transaction data of the target user, and the current session sequence is input into the first prediction model, so that the first prediction model abstracts the current session sequence into a sequence feature vector based on the attention mechanism, and generates a first recommendation result based on the sequence feature vector, and further obtains the second recommendation result based on the first recommendation result and the user feature information, and then determines the target transaction combination recommended to the target user based on the second recommendation result. In the embodiment of the present application, the current session sequence of the target user is predicted based on the transaction sequence features by the first prediction model, and the user feature information is integrated into the first recommendation result predicted based on the transaction sequence features to achieve prediction based on user features, so as to provide accurate and personalized transaction recommendation services to the target user.

[0097] Optionally, a possible implementation method of obtaining the second recommendation result according to the first recommendation result and the user characteristic information in step S203 may be: inputting the first recommendation result and the user characteristic information into a second prediction model for recommendation transaction prediction, and obtaining the second recommendation result output by the second prediction model.

[0098] Exemplarily, the second prediction model may be any one of a k-nearest neighbor (KNN) classification algorithm, a random forest algorithm, and a support vector machine.

[0099] The embodiment of the present application integrates user feature information through the second prediction model to achieve personalized recommendation of transactions.

[0100] Optionally, a possible implementation method of inputting the first recommendation result and user characteristic information into the second prediction model for recommendation transaction prediction may be: screening the first recommendation result to obtain a third recommendation result, the third recommendation result including at least two first recommended transactions; inputting the third recommendation result and user characteristic information into the second prediction model for recommendation transaction prediction.

[0101] In a possible implementation, multiple recommended transactions included in the first recommendation result are arranged in descending order according to the recommendation score corresponding to each recommended transaction, and at least two recommended transactions with higher recommendation scores are selected as the third recommendation result.

[0102] Combine the following Figure 5 The specific implementation method of step S204 of determining the target transaction combination recommended to the target user according to the second recommendation result is described in detail.

[0103] Figure 5 Schematic diagram of the process of the transaction combination recommendation method provided in the embodiment of the present application Figure 2 .like Figure 5 As shown, according to the second recommendation result, a specific implementation method of determining the target transaction combination recommended to the target user may include the following steps:

[0104] S501 : Selecting a second recommended transaction with the highest recommendation probability as a target recommended transaction from a plurality of second recommended transactions included in the second recommendation result.

[0105] Exemplarily, the second recommendation result includes a plurality of different second recommended transactions and a recommendation probability corresponding to each second recommended transaction.

[0106] S502: Determine whether at least two first recommended transactions include a target recommended transaction.

[0107] Exemplarily, determine whether the target recommended transaction is the same as any first recommended transaction of at least two first recommended transactions. If any first recommended transaction is the same as the target recommended transaction, it means that the target recommended transaction is included in the at least two first recommended transactions; if no first recommended transaction is the same as the target recommended transaction, it means that the target recommended transaction is not included in the at least two first recommended transactions.

[0108] In this step, when it is determined that the target recommended transaction is included in the at least two first recommended transactions, step S503 is executed; when it is determined that the target recommended transaction is not included in the at least two first recommended transactions, step S504 is executed.

[0109] S503, selecting a second recommended transaction with the second highest recommendation probability as a target recommended transaction from the multiple second recommended transactions included in the second recommendation result, and repeating the steps to determine whether the target recommended transaction is included in at least two first recommended transactions.

[0110] It can be understood that when selecting the target recommended transaction from the second recommendation results, the recommendation probability corresponding to each second recommended transaction included in the second recommendation results is selected in descending order, and the selected second recommended transaction that is not included in at least two first recommended transactions is determined as the target recommended transaction.

[0111] Exemplarily, when the second recommended transaction corresponding to the maximum recommendation probability is included in at least two first recommended transactions, the second recommended transaction ranked second in descending order of recommendation probability is selected from the second recommendation results as the target recommended transaction, and it is determined whether the current target recommended transaction is included in at least two first recommended transactions. Further, when it is included in at least two first recommended transactions, the second recommended transaction ranked third in descending order of recommendation probability is selected from the second recommendation results as the target recommended transaction, and so on, until the selected target recommended transaction is not included in at least two first recommended transactions.

[0112] S504: Determine the target recommended transaction and at least two first recommended transactions as a target transaction combination.

[0113] In the embodiment of the present application, the second recommended transaction with the highest recommendation probability is selected as the target recommended transaction from the multiple second recommended transactions included in the second recommendation result, and it is determined whether the target recommended transaction is included in at least two first recommended transactions. If it is included, the second recommended transaction with the second highest recommendation probability is selected as the target recommended transaction from the multiple second recommended transactions included in the second recommendation result, and the steps are repeated to determine whether the target recommended transaction is included in at least two first recommended transactions. If it is not included, the target recommended transaction and at least two first recommended transactions are determined to be a target transaction combination. In the embodiment of the present application, at least two first recommended transactions with higher recommendation scores in the first recommendation result obtained by the first prediction model based on the conversation sequence features and the second recommended transaction with higher recommendation probability and different from the first recommendation in the second recommendation result obtained by the second prediction model by fusing user features are used as the target transaction combination, so as to achieve personalized recommendation services while ensuring the accuracy of the recommended transactions.

[0114] Optionally, the user characteristic information is obtained by: obtaining an identity identifier of a target user; determining a customer group corresponding to the target user according to the identity identifier; and obtaining user characteristic information corresponding to the customer group according to the customer group.

[0115] In order to better integrate user characteristics and implement personalized transaction combination recommendations for users, in one possible implementation, users can be grouped according to the age of users using the self-service terminal to obtain customer groups of different age groups, and further for different customer groups, different weight coefficients are added to each user characteristic in the customer group, such as financial assets and gender, to obtain user characteristic information.

[0116] Exemplarily, the customer groups may include teenagers, young adults, middle-aged people, middle-aged and elderly people.

[0117] For example, users aged between 0 and 7 years old can be classified as teenagers, users aged between 7 and 18 years old can be classified as young people, users aged between 18 and 46 years old can be classified as middle-aged, users aged between 46 and 60 years old can be classified as middle-aged and elderly, and users aged over 60 years old can be classified as elderly.

[0118] Exemplarily, user characteristic information can be expressed by the following formula:

[0119]

[0120] Among them, α 1,1 etc. represent different weight coefficients, and X1 etc. represent user features.

[0121] Combine the following Figure 6 The training methods of the first prediction model and the second prediction model in the transaction combination recommendation method provided in the embodiment of the present application are described in detail.

[0122] Figure 6 A flowchart of the first prediction model and the second preset model training method provided in the embodiment of the present application. Figure 6 As shown, the implementation method of the training method of the first prediction model and the second preset model specifically includes the following steps:

[0123] S601, obtaining the user's historical transaction data and user characteristic information.

[0124] Exemplarily, historical transaction data of the user is obtained from a transaction database based on self-service terminal transactions.

[0125] For example, the historical transaction data may be Figure 1 Detailed transaction data of level 3 transactions and above as shown in .

[0126] The method of obtaining user characteristic information is similar to the above, and will not be repeated here.

[0127] S602, pre-processing the historical transaction data to obtain multiple session sequences, the pre-processing including screening processing and conversion processing.

[0128] Exemplarily, the screening process may be to filter out the transaction data corresponding to each user in the historical transaction data, the data in which the user uses the self-service terminal only once for a business transaction, and the transaction data in which the cumulative number of clicks is less than 5.

[0129] Exemplarily, the conversion process may be to first convert each transaction data of the user into a corresponding secondary transaction according to the mapping relationship between the tertiary transaction ID and the secondary transaction ID, and then convert the transaction data into a session sequence according to the sequence of business transactions clicked in the transaction data.

[0130] Table 1 is the data preprocessing results provided in the examples of the present application.

[0131] Table 1

[0132] Serial number Session Sequence Trading Tags User ID Financial assets gender age 0 [1,2,3,4,5] 1 0 3 2.0 6 1 [1,2,3,4] 5 0 3 2.0 6 2 [1,2,3] 4 0 3 2.0 6 3 [1,2] 3 0 3 2.0 6 4 [1] 2 0 3 2.0 6

[0133] As shown in Table 1, the transaction data includes the user's age, gender, financial assets, user ID, transaction tags, and session sequence.

[0134] As shown in Table 1, there is a certain association between transaction tags. For example, the transaction tag associated with transaction tag 1 is transaction tag 2, the transaction tag associated with transaction tag 2 is transaction tag 3, the transaction tag associated with transaction tag 3 is transaction tag 4, and so on. That is, when the transaction tag corresponding to the last clicked business transaction in the session sequence is transaction tag 1, the transaction tag of the next click in the session sequence is transaction tag 2; when the transaction tag corresponding to the last clicked business transaction in the session sequence is transaction tag 2, the transaction tag of the next click in the session sequence is transaction tag 3, and so on.

[0135] S603, for each conversation sequence in the multiple conversation sequences, input the conversation sequence into the first prediction model to perform recommended transaction prediction, obtain a first predicted transaction output by the first prediction model, and based on a first loss function, obtain a first loss value between the first predicted transaction and the true label, and adjust the model parameters of the first prediction model according to the first loss value.

[0136] The first prediction model is similar to the above and will not be described in detail here.

[0137] Exemplarily, each conversation sequence corresponds to a conversation graph, and the first loss function of each conversation graph may be a cross entropy between a predicted value and a true value.

[0138] Exemplarily, the first loss function can be expressed by the following formula:

[0139]

[0140] in, represents the cross entropy, y i represents the real transaction corresponding to the i-th session sequence, Represents the predicted transaction corresponding to the i-session sequence.

[0141] S604, inputting the first predicted transaction and user feature information into the second prediction model to perform recommended transaction prediction, obtaining a second predicted transaction output by the second prediction model, and based on a second loss function, obtaining a second loss value between the second predicted transaction and the true label, and adjusting the model parameters of the second prediction model according to the second loss value.

[0142] It can be understood that in the transaction combination recommendation method provided in the embodiment of the present application, the first prediction model and the second prediction model are obtained by joint training.

[0143] Figure 7 A schematic diagram of the structure of the joint training of the first prediction model and the second prediction model provided in the embodiment of the present application. Figure 7 As shown, the first prediction model 71 is trained by obtaining a conversation sequence based on the user's historical transaction data, and the first predicted transaction and user feature information output by the first prediction model 71 are input into the second prediction model 72 to train the second prediction model 72.

[0144] In a possible implementation, the first three predicted transactions with the highest prediction scores in the first predicted transaction and the user feature information may be concatenated and input into the second prediction model to train the second prediction model.

[0145] Exemplarily, the second loss function is calculated based on the second predicted transaction and the real transaction labels of the transactions in the conversation sequence. The real transaction labels are similar to the transaction labels shown in Table 1 above, and are not described here in detail.

[0146] The second prediction model is similar to the above one and will not be described in detail here.

[0147] Compared with the related art, in the process of model training, the user features are often screened for several fixed features, such as gender, age, region, etc. However, different groups of people have different importance on these features. In the transaction combination recommendation method provided in the embodiment of the present application, by grouping users, adding appropriate weights to the corresponding user features for different customer groups, and then training the second prediction model based on the obtained user feature information, the prediction accuracy of the model can be improved.

[0148] In the embodiment of the present application, by obtaining the user's historical transaction data and user feature information, the historical transaction data is screened and converted to obtain multiple session sequences. For each session sequence in the multiple session sequences, the session sequence is input into the first prediction model for recommended transaction prediction to obtain the first predicted transaction output by the first prediction model, and based on the first loss function, the first loss value between the first predicted transaction and the true label is obtained, and the model parameters of the first prediction model are adjusted according to the first loss value; the first predicted transaction and user feature information are input into the second prediction model for recommended transaction prediction to obtain the second predicted transaction output by the second prediction model, and based on the second loss function, the second loss value between the second predicted transaction and the true label is obtained, and the model parameters of the second prediction model are adjusted according to the second loss value. In the embodiment of the present application, by using historical transaction data to train the first prediction model, the accuracy of the first prediction model for predicting recommended transactions based on sequence features is improved, and the output result of the first prediction model is further integrated with the user feature information, and the second prediction model is trained to improve the prediction ability of the second prediction model for personalized transaction recommendations.

[0149] In summary, in the transaction combination recommendation method provided in the embodiment of the present application, on the one hand, the first prediction model can be used to extract implicit features between transactions from complex historical self-service terminal transaction data, and prediction based on transaction sequences can be achieved to better capture user combination transaction needs and provide accurate transaction recommendation services; on the other hand, the second prediction model is used to integrate user feature information related to the user's self-service transaction terminal to achieve personalized combination recommendations for self-service terminal transactions. In summary, the user experience and recognition of business transactions based on self-service terminals can be improved, the limitations of the use of self-service terminals can be reduced, and the utilization rate of self-service terminals can be improved, thereby promoting the development of financial services in the direction of digitalization and intelligence.

[0150] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0151] Figure 8 This is a schematic diagram of the structure of the transaction combination recommendation device provided in the embodiment of the present application. Figure 8 As shown, the transaction combination recommendation device 80 includes an acquisition module 810 , a generation module 820 , a prediction module 830 and a determination module 840 .

[0152] The acquisition module 810 is used to acquire the current session sequence and user feature information of the target user, where the current session sequence is obtained by converting the current transaction data of the target user;

[0153] A generating module 820 is used to input the current conversation sequence into a first prediction model, the first prediction model abstracts the current conversation sequence into a sequence feature vector based on an attention mechanism, and generates a first recommendation result based on the sequence feature vector;

[0154] Prediction module 830, used to obtain a second recommendation result according to the first recommendation result and user characteristic information;

[0155] The determination module 840 is used to determine the target transaction combination recommended to the target user according to the second recommendation result.

[0156] In a possible implementation, the prediction module 830 is specifically used to: input the first recommendation result and user feature information into a second prediction model to perform recommendation transaction prediction, and obtain a second recommendation result output by the second prediction model.

[0157] In a possible implementation, the prediction module 830 is also used to: filter the first recommendation result to obtain a third recommendation result, where the third recommendation result includes at least two first recommended transactions; and input the third recommendation result and user feature information into the second prediction model to perform recommended transaction prediction.

[0158] In one possible implementation, the determination module 840 is specifically used to: select the second recommended transaction with the highest recommendation probability as the target recommended transaction from the multiple second recommended transactions included in the second recommendation result; determine whether the target recommended transaction is included in at least two first recommended transactions; if included, select the second recommended transaction with the second highest recommendation probability from the multiple second recommended transactions included in the second recommendation result as the target recommended transaction, and repeat the steps to determine whether the target recommended transaction is included in at least two first recommended transactions; if not included, determine the target recommended transaction and at least two first recommended transactions as the target transaction combination.

[0159] In a possible implementation, the user characteristic information is obtained by: obtaining an identity identifier of a target user; determining a customer group corresponding to the target user based on the identity identifier; and obtaining user characteristic information corresponding to the customer group based on the customer group.

[0160] In a possible implementation, the first prediction model and the second preset model are trained in the following manner: obtaining the user's historical transaction data and user feature information; preprocessing the historical transaction data to obtain multiple session sequences, the preprocessing including screening and conversion; for each session sequence in the multiple session sequences, inputting the session sequence into the first prediction model for recommended transaction prediction, obtaining a first predicted transaction output by the first prediction model, and based on a first loss function, obtaining a first loss value between the first predicted transaction and the true label, and adjusting the model parameters of the first prediction model according to the first loss value; inputting the first predicted transaction and user feature information into the second prediction model for recommended transaction prediction, obtaining a second predicted transaction output by the second prediction model, and based on a second loss function, obtaining a second loss value between the second predicted transaction and the true label, and adjusting the model parameters of the second prediction model according to the second loss value.

[0161] The transaction combination recommendation device provided in this embodiment can be used to execute the method steps of the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0162] Fig. 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig. 9 As shown, the electronic device 90 includes: at least one processor 910, a memory 920, a communication interface 930 and a system bus 940. The memory 920 and the communication interface 930 are connected to the processor 910 through the system bus 940 and communicate with each other. The memory 920 is used to store instructions, the communication interface 930 is used to communicate with other devices, and the processor 910 is used to call the instructions in the memory to execute the method steps provided in the above method embodiment. The specific implementation methods and technical effects are similar and will not be repeated here.

[0163] Should Fig. 9 The system bus 940 mentioned in the figure can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus 940 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0164] The communication interface 930 is used to implement communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries).

[0165] The memory 920 may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0166] The processor 910 may be a general-purpose processor, including a central processing unit, a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0167] The embodiment of the present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method steps in the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0168] The present application also provides a computer program product, which includes computer executable instructions. When the computer executable instructions are executed, the method steps in the above method embodiment are implemented. The specific implementation method and technical effect are similar and will not be repeated here.

[0169] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0170] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0171] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0172] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0173] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0174] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0175] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0176] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0177] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A transaction combination recommendation method, characterized in that: include: Acquire a current session sequence and user feature information of a target user, wherein the current session sequence is obtained by converting current transaction data of the target user; Inputting the current conversation sequence into a first prediction model, the first prediction model abstracting the current conversation sequence into a sequence feature vector based on an attention mechanism, and generating a first recommendation result based on the sequence feature vector; Obtaining a second recommendation result according to the first recommendation result and the user characteristic information; A target transaction combination recommended to the target user is determined according to the second recommendation result.

2. The transaction combination recommendation method according to claim 1, characterized in that: The obtaining a second recommendation result according to the first recommendation result and the user characteristic information includes: The first recommendation result and the user characteristic information are input into a second prediction model to perform recommendation transaction prediction, and a second recommendation result output by the second prediction model is obtained.

3. The transaction combination recommendation method according to claim 2, characterized in that: The step of inputting the first recommendation result and the user characteristic information into a second prediction model to perform recommendation transaction prediction includes: Screening the first recommendation result to obtain a third recommendation result, wherein the third recommendation result includes at least two first recommended transactions; The third recommendation result and the user characteristic information are input into a second prediction model to perform recommendation transaction prediction.

4. The transaction combination recommendation method according to claim 3, characterized in that: The step of determining the target transaction combination recommended to the target user according to the second recommendation result includes: From the plurality of second recommended transactions included in the second recommendation result, selecting the second recommended transaction with the highest recommendation probability as the target recommended transaction; determining whether the at least two first recommended transactions include the target recommended transaction; If included, selecting the second recommended transaction with the second highest recommendation probability from the multiple second recommended transactions included in the second recommendation result as the target recommended transaction, and repeating the steps to determine whether the at least two first recommended transactions include the target recommended transaction; If not, the target recommended transaction and the at least two first recommended transactions are determined to be the target transaction combination.

5. The transaction combination recommendation method according to any one of claims 1 to 4, characterized in that: The user characteristic information is obtained in the following manner: Get the target user's identity. Determining the customer group corresponding to the target user according to the identity identifier; According to the customer group, user characteristic information corresponding to the customer group is obtained.

6. The transaction combination recommendation method according to any one of claims 1 to 4, characterized in that: The first prediction model and the second preset model are obtained by training in the following manner: Obtain the user's historical transaction data and user feature information; Preprocessing the historical transaction data to obtain multiple session sequences, wherein the preprocessing includes screening processing and conversion processing; For each session sequence in the multiple session sequences, input the session sequence into a first prediction model to perform a recommended transaction prediction, obtain a first predicted transaction output by the first prediction model, obtain a first loss value between the first predicted transaction and a true label based on a first loss function, and adjust a model parameter of the first prediction model according to the first loss value; The first predicted transaction and the user feature information are input into a second prediction model to perform recommended transaction prediction, and a second predicted transaction output by the second prediction model is obtained. Based on a second loss function, a second loss value between the second predicted transaction and the true label is obtained, and according to the second loss value, the model parameters of the second prediction model are adjusted.

7. A transaction combination recommendation device, characterized in that: include: An acquisition module, used to acquire a current session sequence and user feature information of a target user, wherein the current session sequence is obtained by converting current transaction data of the target user; A generation module, configured to input the current conversation sequence into a first prediction model, wherein the first prediction model abstracts the current conversation sequence into a sequence feature vector based on an attention mechanism, and generates a first recommendation result based on the sequence feature vector; A prediction module, configured to obtain a second recommendation result based on the first recommendation result and the user characteristic information; A determination module is used to determine a target transaction combination recommended to the target user according to the second recommendation result.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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